Forthcoming articles


International Journal of Bio-Inspired Computation


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International Journal of Bio-Inspired Computation (62 papers in press)


Regular Issues


  • An Adaptive Coevolutionary Memetic Algorithm for Examination Timetabling Problems   Order a copy of this article
    by Maoguo Gong 
    Abstract: In this paper, we present an adaptive coevolutionary memetic algorithm (ACMA) for examination timetabling problems. In our proposed algorithm, the evolutionary search is conducted in two spaces: the heuristic space and the solution space. In the heuristic space, a hyper-heuristic approach is utilized to generate the initial population, and then basic evolutionary operators are used to find the potential heuristic lists. The evolutionary strategy in the heuristic space is regarded as a global search procedure. In the solution space, according to the solution structure, some specific evolutionary operators are designed for expanding the scope of search in solution space. This scheme can be viewed as the local search procedure. By combining two different strategies, the cooperation between them will eventually increase the diversities in the population. In order to determine which space should be selected at each generation, an adaptive parameter is designed based on the proportion of feasible solutions in the current population. Experimental results demonstrated that ACMA obtains competitive results and outperforms the compared approaches on some benchmark instances.
    Keywords: Evolutionary algorithm; Memetic algorithm; Hyper-heuristic approach; Examination timetabling problem.
    DOI: 10.1504/IJBIC.2015.10004318
  • Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems   Order a copy of this article
    by Gai-Ge Wang, Suash Deb, Leandro Dos Santos Coelho 
    Abstract: Earthworms are essential animals that aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms in nature. In EWA, the offspring are generated through Reproduction 1 and Reproduction 2 independently, and then, we used weighted sum of all the generated offsprings to get the final earthworm for next generation. Reproduction 1 generates only one offspring by itself that is also special kind of reproduction in nature. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully done by nine improved crossover operators that are an extended version of classical crossover operator used in DE (differential evolution) and GA (genetic algorithm). With the aim of escaping from local optima and improving the search ability of earthworms, the addition of a Cauchy mutation (CM) is made to the EWA method. In order to show the robustness of EWA method, nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed and they are compared between each other through twenty-two high-dimensional benchmarks. The results show that EWA23 (Uniform crossover operator is used in Reproduction 2) performs the best and is further benchmarked on forty-eight functions and an engineering optimization case. The EWA method is able to find the better function values on most benchmark optimization problems than seven other metaheuristic algorithms.
    Keywords: Earthworm optimization algorithm; Evolutionary computation; Benchmark functions; Improved crossover operator.
    DOI: 10.1504/IJBIC.2015.10004283
  • Ant Colony Optimization for the Routing Problem in the Constellation Network with Node Satellite Constraint   Order a copy of this article
    by Jing Li, Liangjun Ke 
    Abstract: In the field of satellite communication, there is a trend toward interconnecting satellites into a heterogeneous network. Inter-satellite links (ISLs) are used to transmit satellite management data, such as telemetry and telecommand data, as well as application data. The ground stations act as the management entity, which are connected with node satel-lites. This paper considers a routing problem with the aim of combining satellite network with ground station management network to make integrated planning under the node satellite constraint. Based on the characteristic of the routing problem, ant colony optimization is adopted to solve it. Two solution construction methods, i.e., the sequential method and concurrent method, are proposed. Experimental results on real data show that the concurrent ACO algorithm is better, and it can provide better data transmitting bandwidth of ISLs and save ground station management resources, making the algorithm suitable for real requirement.
    Keywords: satellite network; inter-satellite links; routing; network management; node satellite.
    DOI: 10.1504/IJBIC.2015.10004286
  • New Coral Reefs-based Approaches for the Model Type Selection Problem: A Novel Method to Predict a Nation's Future Energy Demand   Order a copy of this article
    by Sancho Salcedo-Sanz, Jesús Muñoz-Bulnes, Mark Vermeij 
    Abstract: In this paper we describe two new methods to address the Model Type Selection Problem (MTSP) based on bio-inspired computational approaches, derived from algorithms originally used to study coral reef dynamics. The effectiveness of these novel approaches is subsequently illustrated in a problem of energy demand estimation in Spain. The two novel methodologies proposed to address MTSP are derived from the Coral Reefs Optimization (CRO) algorithm, which was originally designed to study community development on coral reefs, but can also be applied to optimization problems. First, we describe how coral species can be defined in the CRO algorithm, in such a way that they can be replaced by competing models to define a MTSP. Through parallel co-evolution of these models and solving for one population of solutions, the modified CRO is able to solve the MTSP. Second, we propose another method to solve MTSPs by modifying the original CRO with a substrate layer, so that the different models considered can be encoded similarly. This second method to solve the MTSP simplifies the application of the CRO operators. Finally, we evaluate the performance of the two CRO-based algorithms by solving a MTSP consisting of the prediction of future energy demand from macro-economic data in Spain as a case study.
    Keywords: Model Type Selection Problem; Coral Reefs Optimization algorithm; Energy demand estimation; Macroeconomic variables.
    DOI: 10.1504/IJBIC.2017.10004324
  • Dow Jones Index Return Forecasting: Semantics Based Genetic Programming with Local Search Optimizer   Order a copy of this article
    by Mauro Castelli, Leonardo Vanneschi, Leonardo Trujillo, Ales Popovic 
    Abstract: Trade decisions on stock markets have drawn attention of both academia and practice for decades. In particular, making accurate stock price predictions is the pillar of effective decisions in high-velocity environments as the successful prediction of a stock\'s future price (i.e. reducing the forecasting error) could yield significant profit and reduce operational costs. Generally, predictions are based on trend predictions and are driven by various direct and indirect factors. To add to the extant body of knowledge about predicting stock market prices we propose a semantics-based genetic programming framework. The proposed framework blends a recently developed version of genetic programming that uses semantics genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. To analyze the appropriateness of the proposed computational method for the stock market price prediction, we analyzed data related to the Dow Jones index. Experimental results confirm the suitability of the proposed method for predicting stock market prices. In fact, the proposed method produces lower errors with respect to the existing state-of-the art techniques, such as neural network and support vector machines. More importantly, our analysis has shown that embedding a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process and yields fitter models.
    Keywords: Forecasting; Financial Markets; Genetic Programming; Semantics; Local Search.
    DOI: 10.1504/IJBIC.2017.10004325
  • Genetic Algorithm based Feature Selection for classification of land cover changes using combined LANDSAT and ENVISAT images   Order a copy of this article
    by Suresh Kumar Nagarajan, Arun M 
    Abstract: Recent monsoon failures and reduced rain falls urge the environmental and ecology researchers to concentrate on the land cover changes. Significant and efficient way to monitor the land cover changes is satellite image classification. This work describes the combination of remotely sensed data, LANDSAT and ENVISAT images, to improve the classification accuracy. Instead of predictor space, embedding space is considered in the proposed KNNES and SVMES methods and applied for the classification of combined LANDSAT and ENVISAT data sets. Genetic Algorithm (GA) based feature selection is adopted to enhance the proposed classification methods. Classification of land cover changes of the study area are identified as used land, unused land, forest and vegetation. Proposed methods are evaluated by an accuracy analysis which follows good practice recommendations. Accuracy is quantified by reporting standard errors i.e., producer accuracy, user accuracy, omission error and commission error. Performance of the proposed SVM and KNN based methods using GA based feature selection for combined data set is improved significantly and provide overall accuracy 80% and 76% respectively.
    Keywords: KNN; SVM,MOKNN,MOSVM; Geneticrnalgorithm,Classification.
    DOI: 10.1504/IJBIC.2017.10004326
  • SEOA based Optimal Design of Analog CMOS Amplifier Circuits   Order a copy of this article
    by Soumen Mallick, Rajib Kar, Durbadal Mandal, Sakti Ghoshal 
    Abstract: This paper presents an evolutionary optimization methodology based on Social Emotional Optimization Algorithm (SEOA) applied for the optimal designs of two analog CMOS amplifier circuits. In SEOA, human being behaviour is organized for attaining superior position in society. The person with the topmost position in society provides the optimal solution in multi-dimensional search space in this virtual world. To earn the supreme position in society through the assistance and rivalry with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. In this paper the transistors sizes are optimized using SEOA to minimize the total MOS area occupied by the MOS transistors used in the designed circuits and to get better performance parameters of the circuits. The simulation results validate the supremacy of SEOA in terms of global convergence, ability of fine tuning and establish it to be a capable contender for the optimal design of analog CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the efficiency of the proposed SEOA based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters. It is shown that SEOA based design technique for each amplifier circuit yields the least MOS area, and each designed circuit is shown to have the best performance parameters like gain, power dissipation etc., as compared with those of other recently reported literature.
    Keywords: CMOS; Circuit Sizing; Two-stage Op-Amp; Differential Amplifier; SEOA; Evolutionary Optimization Techniques; Low Power Design.
    DOI: 10.1504/IJBIC.2016.10004290
  • Grid connected photovoltaic systems power quality improvement using Adaptive Control Strategy   Order a copy of this article
    by Logeswaran Thangamuthu, Senthilkumar Athappan 
    Abstract: In this paper, an innovative adaptive technique for power quality (PQ) improvement in grid linked Photovoltaic (PV) mechanism is launched. The novel adaptive technique constitutes the integrated accomplishment of the mighty Artificial Bees Colony (ABC) algorithm and the Proportional Integral Derivational (PID) controller. The major objective of the proposed method is to forecast the adaptive gain parameters for the usual and anomalous scenarios in the grid side. In the proposed method, the ABC optimizes the gain parameters of the PID controller for diverse grid power variations by employing the objective function, which is devised in accordance with the error minimization between the actual grid parameters and set point grid parameters. According to the optimized gain parameters of the PID controller, the PQ of the grid side has been augmented. Thereafter, the proposed method is executed in the MATLAB/ Simulink platform and the efficiency in execution is evaluated by means of analysis a contrast with the conventional approaches.
    Keywords: Power Quality; ABC algorithm; PID controller; Photovoltaic; grid; control signals; gain parameters.
    DOI: 10.1504/IJBIC.2016.10004292
  • Syllogistic Reasoning by Strand Algebra   Order a copy of this article
    by Mandrita Mondal, Kumar S. Ray 
    Abstract: In this paper we introduce DNA strand algebra, which can be defined as a branch of process algebra, for modeling dynamic DNA devices called DNA tweezers whose operation is based on the mechanism of DNA strand displacement. The main components of DNA strand algebra are DNA strands, DNA gates, and their interactions. In this paper we demonstrate a DNA fuelled molecular machine for reasoning with dispositions which is basically a challenging problem to handle commonsense reasoning. Finally we have designed a successful model based on the syntax and semantics of DNA strand algebra to perform syllogistic reasoning with DNA tweezers.
    Keywords: DNA strand algebra; process algebra; dispositions; commonsense reasoning; chaining syllogism; molecular computing; strand displacement; DNA tweezers; dispositional modus ponens; usuality.
    DOI: 10.1504/IJBIC.2016.10004293
  • Optimal Power Flow using Clustered Adaptive Teaching Learning Based Optimization   Order a copy of this article
    by Surender Reddy Salkuti 
    Abstract: In this paper, Optimal Power Flow (OPF) with non-convex and non-smooth generator cost characteristics is presented using Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm. The proposed OPF formulation includes active and reactive power constraints; prohibited zones, and valve point loading (VPL) effects of generators. In the problem formulation, transformer tap settings and reactive power compensating devices settings are also considered as control variables. OPF is a complicated optimization problem, hence there is a need to solve this problem with an accurate algorithm. In the proposed CATLBO algorithm, the class is divided into different sections, and allot different teacher to every section depending on the performance of that particular section. This sectioning of the class makes the proposed technique more robust and less prone to trapping in local optima. The OPF solution is obtained by considering generator fuel cost, transmission loss and voltage stability index as objective functions. The effectiveness of proposed CATLBO algorithm is validated on IEEE 30 bus test system, and the simulation results obtained with CATLBO algorithm are compared with other optimization techniques presented in the literature.
    Keywords: Evolutionary algorithms; optimal power flow; voltage profile; voltage stability.
    DOI: 10.1504/IJBIC.2016.10004295
  • A Bacterial Foraging based Batch Scheduling Model for Distributed System   Order a copy of this article
    Abstract: The problem of scheduling in the parallel and distributed environment is proven to be NP-complete and has been addressed by various heuristics. It is always desired from a scheduling scheme to distribute the load evenly on the available resources so as to have maximum resource utilization while meeting the scheduling objective(s). Bio inspired heuristics for job scheduling have gained immense popularity due to their effectiveness in providing near optimal solution in a reasonable time and computational complexity. This work proposes an evolutionary static scheduling technique based on bacterial foraging for a batch of independent jobs. This model generates the schedule minimizing the node idle time and the makespan while exhibiting a balanced load distribution with minimum run time overhead. Simulation study proves the effectiveness of the proposed model in comparison with its peers.
    Keywords: Distributed system; Scheduling; Load Balancing; Bacterial Foraging; Utilization; Makespan.
    DOI: 10.1504/IJBIC.2016.10004297
  • Hybrid Bio-inspired Scheduling Algorithms for Batch of Tasks on Heterogeneous Computing System   Order a copy of this article
    by Mohammad Sajid, Zahid Raza, Mohammad Shahid 
    Abstract: Due to high operational cost, the problem of scheduling a batch of tasks (BoT) applications on HCS remains a challenging problem. Accordingly, a plethora of evolutionary algorithms (EAs) and non-EAs have been proposed as solutions. Due to the ability of exploration of major solution space, EAs have been proven to be very effective in addressing the job scheduling problem. This work proposes two hybrid bio-inspired scheduling algorithms VPG and VDG featuring the combined best properties of VNS, PSO, DE and GA. The expected-time-to-compute (ETC) benchmark have been used to first present the performance of 8 non-EAs viz. MCT, MinMin, MaxMin, Sufferage, HLTF, Relative Cost, MINMin and MINSuff in terms of makespan and energy consumption. The study is then extended to evaluate the performance of VPG, VDG and their seeded variants with GA, PSO and DE. Simulation study establishes the superior performance of VDG over peers.
    Keywords: Bio-inspired Computation; Heterogeneous Computing System (HCS); Batch of Tasks (BoT); Scheduling; Hybrid Evolutionary Algorithms; Makespan; Energy Consumption.
    DOI: 10.1504/IJBIC.2016.10004299
  • A Time-Varying Strategy for Urban Traffic Network Control: A Fuzzy Logic Control Based on an Improved Black Hole algorithm   Order a copy of this article
    by Mohammad Hassan Khooban, Alireza Liaghat 
    Abstract: Urban Traffic Network (UTN) models are usually illustrated by State-Charts (CH) and Object-Diagrams (OD). However, these charts or diagrams have many problems showing the behavioral perspective of Traffic Information flow. Further to the disadvantage of these charts and diagrams is the absence of formalization, which in turn results in uncertainty in Information modeling. As a result, a state space model is usually used to calculate the half-value waiting time of vehicles in UTN with a stabled time control. In this study, a combination of the general type-2 fuzzy logic sets (GT2FLS) and the Modified Black Hole (MBH) Algorithm techniques is used in order to control the traffic signal scheduling and phase succession so as to guarantee the smooth flow of traffic with the least waiting time and average queue length. The parameters of input and output membership functions are optimized simultaneously by a novel heuristic algorithm, namely the Modified Black Hole (MBH) Algorithm. A comparison is made between the results obtained regarding the performance of the proposed model and those of the Optimal Type-1 Fuzzy Logic (OT1FL) and Conventional Type-1 Fuzzy Logic (CT1FL) controllers, which are the most recent researches in the problem at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller.
    Keywords: Traffic Signal Control; Optimal General Type-2 Fuzzy Controller (OGT2FC); Modified Backtracking Search Algorithm (MBSA).
    DOI: 10.1504/IJBIC.2016.10004303
  • Hybrid Symbiotic Organisms Search Algorithm for Solving 0-1 Knapsack Problem   Order a copy of this article
    by Yongquan Zhou 
    Abstract: We propose a new binary version of hybrid symbiotic organisms search algorithm based on harmony search with greedy strategy for solving 0-1 knapsack problems. A greedy strategy is employed to repair the infeasible solution and optimize the feasible solution. The experiments are carried out in small-scale and large-scale knapsack problem instances. We report on computational experiments which are compared with the results achieved with other state-of-the-art approaches. The results attest the performance of our approach.
    Keywords: Knapsack problem; Symbiotic organisms search; Harmony search; Greedy strategy.
    DOI: 10.1504/IJBIC.2016.10004304
  • An improved artificial bee colony algorithm for global numerical optimisation   Order a copy of this article
    by Tahere Yaghoobi, Elahe Esmaeili Toudeshki 
    Abstract: Artificial bee colony (ABC) is a simple and powerful optimisation algorithm, which simulates random behaviour of honey bees. In this study, a modified version of ABC algorithm is proposed by considering (1) initializing the population based on chaos theory, (2) utilising multiple searches, instead of single search, in employee and onlooker bee phases, and (3) controlling the frequency of perturbation by a modification rate. The proposed algorithm implemented by C# programming language and executed on benchmark functions of Sphere, Rosenbrock, Rastrigin, Non-
    Keywords: Artificial bee colony algorithm; chaos; metaheuristics; optimisation; swarm intelligence.
    DOI: 10.1504/IJBIC.2016.10004305
  • Encodings and Genetic Operators for Efficient Evolutionary Design of Digital Circuits - new   Order a copy of this article
    by Nadia Nedjah, Luiza Mourelle 
    Abstract: The performance of evolutionary computation based methods, such as genetic algorithms, depends primarily on the fitness function. Nonetheless. it is also critically impacted by the choice of the internal representation of potential solutions together with the way the genetic operators such as crossover and mutation are implemented. With this motivation, in this work we analyze the impact of two different circuit encodings used for digital circuit evolution on the process performance. The outcome of this study is mainly to elect the encoding and corresponding genetic operators that yield efficient digital circuits regarding the specified cost function, yet it does so in an agile efficient evolutionary process. The first investigated approach is based on genetic programming, wherein digital circuits consist of their data flow based specifications. In this approach, individuals are internally represented by the abstract trees of the corresponding circuit specifications. In the second investigated approach, digital circuits are viewed as a map of rooted gates. So potential solutions are represented by two-dimensional arrays of cells. Each of these cells includes the logic gate descriptor together with the corresponding input signal identifiers. Furthermore, we compare the impact of both circuit representations on the evolutionary process of digital designs. Evolved circuits must implement the exact expected behavior. Yet, they should minimize hardware and time requirements. We show that for the same input/output behavior, employing both approaches yields circuits of similar characteristics in terms of these requirements. However, the evolution process is much efficient in the case the second encoding.
    Keywords: Evolutionary hardware; Encodings; genetic operators; digital circuit.
    DOI: 10.1504/IJBIC.2016.10004306
  • A Mathematical Framework for Possibility Theory Based Hidden Markov Model   Order a copy of this article
    by Neha Baranwal 
    Abstract: Exploring correct pattern from low frequency time series data is challenging. For resolving this problem the concept of possibility theory based Hidden Markov Model (PTBHMM) has been proposed. In this paper all the three fundamental problems (evaluation, decoding and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty we have used axiomatic approach of possibility theory. Time complexity of existing solutions of HMM (Forward, backward, Viterbi, Baum welch) and proposed possibility based solutions have been calculated and compared. From comparison result it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real time applications.
    Keywords: Hidden Markov Model; Possibility Theory; Gesture Recognition; Stochastic Process.
    DOI: 10.1504/IJBIC.2016.10004307
  • Application of Swarm Intelligent Techniques with Mixed Variables to Solve Optimal Power Flow Problems   Order a copy of this article
    by Surender Reddy Salkuti 
    Abstract: This paper proposes a new swarm based evolutionary algorithm called LBEST PSO with dynamically varying sub-swarms (LPSO DVS). Swarm based algorithms are meta-heuristic search methods whose mechanics are inspired by the collaborative behavior of biological populations. The performance of four swarm based algorithms, i.e., Particle Swarm Optimization (PSO), Fuzzy Adaptive Particle Swarm Optimization (FAPSO), Fitness Distance Ratio Particle Swarm Optimization (FDRPSO) and LPSO DVS are also compared with Genetic Algorithm (GA) and Improved GA when applied to the power system Optimal Power Flow (OPF) problem. OPF optimizes the power system operating objective function, while satisfying the set of system operating constraints. The objective functions considered in this OPF problem are Fuel Cost (FC) minimization, Voltage Stability Enhancement Index (VSEI) minimization, transmission loss Minimization (LM) and Voltage Deviation (VD) minimization. Simulation results for the IEEE 30 bus system are presented and the comparison is made among the numerical results obtained using therndifferent evolutionary algorithms.
    Keywords: Evolutionary Algorithms; Fuzzy Sets; Genetic Algorithm; Optimal PowerrnFlow; Particle Swarm Optimization.
    DOI: 10.1504/IJBIC.2016.10004308
  • Economic load dispatch using memory based differential evolution   Order a copy of this article
    by Raghav Prasad Parouha, Kedar Nath Das 
    Abstract: Many variants of Differential Evolution (DE) algorithm and its hybrid versions exist in the literature to solve Economic Load Dispatch (ELD) problem. However, the performance of DE is highly affected by the inappropriate choice of its operators like mutation and crossover. Moreover, in general practice, DE does not employ any strategy of memorizing the best results obtained so far in the initial part of the previous cycle. An attempt is made in this paper to propose a Memory based DE (MBDE) where two swarm operators have been introduced. These operators based on the pBEST and gBEST mechanism of particle swarm optimization (PSO). The proposed MBDE is tested over 4 different power test systems of ELD problem with varying complexities. Numerical, statistical and graphical analysis reveals the competency of the proposed MBDE.
    Keywords: Differential Evolution; Mutation; Crossover; Economic load dispatch problem.
    DOI: 10.1504/IJBIC.2016.10004309
  • A Nature Inspired Hybrid Optimization Algorithm for Dynamic Environment with Real Parameter Encoding   Order a copy of this article
    by Ashish Tripathi, Nitin Saxena, Krishn Mishra, Arun Misra 
    Abstract: During recent years, many nature inspired algorithms have been proposed which are widely applicable for different optimization problems. Real-world optimization problems have become more complex and dynamic in nature and a single optimization algorithm is not good enough to solve such type of problems individually. Thus hybridization of two or more algorithms may be a fruitful effort in handling the limitations of individual algorithm. In this paper a hybrid optimization algorithm has been established which includes the features of Environmental Adaption Method for Dynamic Environment (EAMD) and Particle Swarm Optimization (PSO). This algorithm is specially designed to optimize both unimodal and multimodal problems and the performance is checked over a group of 24 benchmark functions provided by Black Box Optimization Benchmarking (BBOB-2013). The result shows the superiority of this hybrid algorithm over other well established state-of-the-art algorithms.
    Keywords: Adaptive Learning; EAMD; EAM; Hybrid Algorithm; Optimization; PSO.
    DOI: 10.1504/IJBIC.2016.10004310
  • Multi-swarm Cooperative Multi-objective Bacterial Foraging Optimization   Order a copy of this article
    by Ben Niu, Jing Liu, Lijing Tan 
    Abstract: his paper proposes a novel multi-objective algorithm which is based on the concept of master-slave swarm, namely Multi-swarm Cooperative Multi-objective Bacterial Foraging Optimization (MCMBFO). In MCMBFO, the multi-swarm cooperative operation which involves several slave-swarms and a master-swarm is developed to accelerate the bacteria to come closer to the true Pareto front. With regard to slave-swarms, each of them evolves collaboratively with others during the step of chemotaxis and reproduction, using information communication mechanism and cross-reproducing approach to enhance the convergence rate respectively. At the same time, bacteria in the master-swarm are all non-dominated individuals selected from slave-swarms. They evolve based on non-dominated sorting approach and crowding distance operation, aiming to improve the accuracy and diversity of solutions. The superiority of MCMBFO is confirmed by simulation experiments using several test problems and performance metrics chosen from prior representative studies.
    Keywords: Multi-swarm; Multi-objective; Bacterial Foraging Optimization.
    DOI: 10.1504/IJBIC.2016.10004311
  • Programmable DNA-Mediated Decision Maker   Order a copy of this article
    by Jian-Jun SHU 
    Abstract: DNA-mediated computing is a novel technology that seeks to capitalize on the enormous informational capacity of DNA and has tremendous computational ability to compete with the current silicon-mediated computing, due to massive parallelism and unique characteristics inherent in DNA interaction. In this paper, the methodology of DNA-mediated computing is utilized to enrich decision theory, by demonstrating how a novel programmable DNA-mediated normative decision-making apparatus is able to capture rational choice under uncertainty.
    Keywords: DNA; processor; material; programmable biochemical operator.
    DOI: 10.1504/IJBIC.2016.10004312
  • A modified bats echolocation-based algorithm for solving constrained optimisation problems   Order a copy of this article
    by Nafrizuan Mat Yahya, M. Osman Tokhi 
    Abstract: A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems.
    Keywords: Modified adaptive bats sonar algorithm; bats echolocation; constrained optimisation problems.
    DOI: 10.1504/IJBIC.2016.10004313
  • A clustering algorithm based on elitist evolutionary approach   Order a copy of this article
    by Lydia Boudjeloud-Assala, Minh Thuy TA 
    Abstract: The k-means algorithm is a very popular optimization approach in clustering problems. However, while the algorithm is convenient to implement and try to find a stable solution, it produces solutions that are locally optimal. Thus, the k-means algorithm may not achieve the globally optimal solution to clustering problems, which can have a large number of local optimum. At the same time, it strongly depends on the number of clusters k and initialization seeds. This paper introduces a new algorithm designedrnto produce a global optimal solution or near-optimal solution for clustering tasks. The proposed method is based on the cluster number optimization and at the same time, proposes the potential clusters seeds. This method can be used directly as a clustering algorithm or as an initialization of the k-means algorithm. One problem with applying these two approaches directly is the number of parameters required to find an optimal solution. We propose several solutions to this problem. One solution is to apply diversity of population maintained through different evolutionary sub-populations. Thernsecond solution is to apply the Elitist Strategy to select only the best concurrent solution which represents the Elite solution. We also propose a new mutation strategy according to the neighborhood search, which uses an automatic detection of the cluster limit. This cooperative strategy allows us to find the global optimal solution or near-optimal solution for clustering tasks and optimal cluster seeds. We conduct numerical experimentsrnto evaluate the effectiveness of the proposed algorithms in comparison to the classical k-means and k-medoids algorithms. The experimental results show that our algorithm performs well on multi-class data sets, overlapped data sets and large-size data sets.
    Keywords: Data exploration; optimization approach; elitist approach; clusters number; clustering; spherical clusters; ellipsoidal clusters.
    DOI: 10.1504/IJBIC.2016.10004315
  • A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem   Order a copy of this article
    by Teerapun Saeheaw 
    Abstract: The vehicle routing problem involves distribution management in the fields of transportation, distribution, and logistics, and it is one of the most important, and studied, combinatorial optimization problems. The capacitated vehicle routing problem is an NP-hard problem, which was introduced by Dantzig and Ramser in 1959. The objective is to minimize the total distance and to maximize capacity for all of the vehicles. In this paper, the proposed parallel hybrid artificial intelligent approaches are based on cuckoo search that uses the positive features of two other optimization techniques, central force optimization and chemical reaction optimization, for enhancing local search and improving the quality of the initial population, respectively. The motivation for this work is to improve the computational efficiency by getting even better results than the previous best known solutions, to study of the dynamics of various parameters of proposed approaches in searching optimum solutions, and to quicken the process of finding the optimal solution. The proposed approaches are tested on standard test instances from the literature. The test results demonstrate the effectiveness of the proposed approaches in solving the capacitated vehicle routing problem efficiently.
    Keywords: capacitated vehicle routing problem; CVRP; cuckoo search; CS; central force optimization; CFO; chemical reaction optimization; CRO.
    DOI: 10.1504/IJBIC.2016.10004317
  • A Rule Generation Algorithm from Neural Network using Classified and Misclassified Data   Order a copy of this article
    by Saroj Biswas, Manomita Chakraborty, Biswajit Purkayastha 
    Abstract: Classification is one of the important tasks of data mining and neural network is one of the best known tools for doing this task. Despite of producing high classification accuracy, the black box nature of neural network makes it useless for many applications which require transparency in its decision making process. This drawback is overcome by extracting rules from neural network. Rule extraction makes neural network an alternative to other machine learning methods for handling classification problems by deriving an explanation of how each decision is made. Till now many algorithms on rule extraction have been proposed but still research on this area is going on to find out more accurate and understandable rules. The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses classified and misclassified patterns to find out the data ranges of significant attributes in respective classes. The experimental results clearly show that the proposed algorithm produces accurate and understandable rules compared to existing algorithms.
    Keywords: Data Mining; Artificial Neural networks; Rule extraction; Pedagogical; RxREN algorithm; Classification.
    DOI: 10.1504/IJBIC.2016.10004336
  • Behavior-driven Dynamic Pricing Modeling via Hidden Markov Model   Order a copy of this article
    by Qinfu Qiu, Xiong Chen 
    Abstract: Abstract --- The dynamic pricing strategy in airline tickets has gained a lot of concern from both air companies and customers. As it has been proved that putting out discount in airline tickets in sometime may increase a companys total revenue. In this paper, after making analysis in both sides of airline and passengers behavior, we found that all the different choices made by passengers, whether purchase or keep waiting, come from an invisible logical chain which contains several key driving elements. Thus, we implement hidden Markov model here, trying to model a new pricing mechanism to raise the revenue. The simulation verified this models practicability.
    Keywords: Key Words --- dynamic pricing strategy; revenue; invisible logical chain; hidden Markov model.
    DOI: 10.1504/IJBIC.2016.10004341
  • Bacterial Foraging Optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm: a comparative study   Order a copy of this article
    by Soheila Sadeghiram 
    Abstract: Nature inspired meta-heuristic algorithms have been widely used in order to find efficient solutions for optimization problems, and granted results have been achieved. Particle swarm optimization algorithm (PSO) is one of the most utilized algorithms in recent years, which has indicated acceptable efficiency. On the other hand, Bacterial Foraging Optimization Algorithm (BFOA) is relatively new compared to other meta-heuristic algorithms, and like PSO has shown a good ability to solve different optimization problems. Genetic Algorithms (GAs) are a well-known group of meta-heuristic algorithms which have been in use earlier than the other in various research fields. In this paper, we compare the efficiency of BFOA and PSO algorithms in an identical condition by minimizing different test functions (from two to 20 dimensional). In this experiment, GA is used as a basic method in order for comparing the two algorithms. The methodology and results are presented. Although results verify the accurate convergency of both algorithms, the efficiency of BFOA on high-dimensional functions is dramatically better than that of PSO.
    Keywords: Particle Swarm Optimization algorithm; Bacterial Foraging Optimization Algorithm; Genetic Algorithms; high-dimensional functions.
    DOI: 10.1504/IJBIC.2016.10004342
  • Differential Evolution Based on Node Strength   Order a copy of this article
    by Lenka Skanderova, Tomas Fabian, Ivan Zelinka 
    Abstract: In this paper, three novel algorithms for optimization based on the differential evolution algorithm are devised. The main idea behind those algorithms stems from the observation that differential evolution dynamics can be modeled via complex networks. In our approach, the individuals of the population are modeled by the nodes and the relationships between them by the directed lines of the graph. Subsequent analysis of non-trivial topological features further influence the process of parent selection in the mutation step and replace the traditional approach which is not reflecting the complex relationships between individuals in the population during evolution. This approach represents a general framework which can be applied to various kinds of differential evolution algorithms. We have incorporated this framework with the three well-performing variants of differential evolution algorithms to demonstrate the effectiveness of our contribution with respect to the convergence rate. Two well-known benchmark sets (including 49 functions) are used to evaluate the performance of the proposed algorithms. Experimental results and statistical analysis indicate that the enhanced algorithms perform better or at least comparable to their original versions.
    Keywords: Differential Evolution Dynamics; Complex Network; Node Strength; Hybrid Mutation Operator; Self-Adapting Parameter.
    DOI: 10.1504/IJBIC.2016.10004343
  • Computer Aided Detection and Classification of Pap smear Cell Images Using Principal Component Analysis   Order a copy of this article
    by Sukumar Ponnusamy, Ravi Samikannu 
    Abstract: Pap smear is a screening methodology employed in cervix cancer detection and diagnosis. The Pap smear images of cervical region are used to detect the abnormality of the cervical cells. In this paper, the computer aided automatic detection and classification method for papsmear cell images are proposed. The cell nucleus is segmented using watershed segmentation methodology and features are extracted from segmented cell nucleus papsmear image. The extracted features are classified using Principal component analysis method. The proposed system classifies the test papsmear cell image into Dysplastic (D), parabasal (P) and Superficial (S) cell images for cervical cancer diagnosis.rn
    Keywords: papsmear; cell nucleus; watershed; cervical cancer; features.rn.
    DOI: 10.1504/IJBIC.2016.10004345
  • An Elite Opposition-Flower Pollination Algorithm for a 0-1 Knapsack Problem   Order a copy of this article
    by Mohamed Abdel Basset, Yongquan Zhou 
    Abstract: The knapsack problem is one of the most studied combinatorial optimization problems with various practical applications. In This paper, we apply an elite opposition-flower pollination algorithm (EFPA), to solve 0-1 knapsack problem, an NP-hard combinatorial optimization problem. The performance of the proposed algorithm is tested against a set of benchmarks of knapsack problems. Computational experiments with a set of large-scale instances show that the EFPA can be an efficient alternative for solving 01 knapsack problems.
    Keywords: Flower pollination algorithm; Meta-heuristics; Combinatorial optimization ; NP-hard ; Optimization; knapsack problems.
    DOI: 10.1504/IJBIC.2016.10004350
  • TRUST-TECH-Enhanced Differential Evolution Methodology for Box-Constrained Nonlinear Optimization   Order a copy of this article
    by Xuexia Zhang 
    Abstract: Box-constrained nonlinear optimization is a class of global optimization problems that is found in many applications. The differential evolution algorithm and its variants have been developed to solve box-constrained optimization problems with encouraging results. However, similar to other heuristic methods implementing stochastic searches, differential evolution still suffers from its poor ability to zoom in on promising regions to find a high-quality or global optimal solution. Transformation Under Stability-reTraining Equilibrium Characterization (TRUST-TECH) methodology is an updated global optimization method that is able to find multiple local optimal solutions to a nonlinear optimization problem in a deterministic and tier-by-tier manner. This paper presents a TRUST-TECH-enhanced differential evolution methodology (TT-DEM) to improve the performance of differential evolution methodology (DEM). In TT-DEM, a differential evolution method is carried out to find the promising region containing a high-quality or global optimal solution, while TRUST-TECH is additionally performed to fully exploit the promising region. Following this framework, the original differential evolution (DE) and three adaptive DEs are enhanced by TRUST-TECH. Numerical studies are conducted on benchmark functions, and the results demonstrate that TRUST-TECH can significantly improve the performance.
    Keywords: Differential evolution (DE); TRUST-TECH; global optimization;hybrid methods.
    DOI: 10.1504/IJBIC.2017.10004355
  • Optimization Inspiring from Behavior of Raining in Nature: Droplet Optimization Algorithm   Order a copy of this article
    by Hamid Parvin 
    Abstract: In this paper one of these methods has been proposed called droplet optimization algorithm (DOA). DOA emulates rainfall phenomenon. It employs some special operators to describe the droplet process, including droplet generation, droplet fall, droplet collision, droplet flowing and droplet updating. To compare performance of this algorithm against those of some up-to-dated optimization algorithms, all of the CEC 2005 contest benchmark functions have been employed. The experimental results have proven that DOA superior to all basic optimization algorithms and also some up-to-dated optimization algorithms.
    Keywords: Optimizer; DOA; Metaheuristics; General Optimization.

  • Bit Mask Oriented Genetic Algorithm for Grammatical Inference and Premature Convergence   Order a copy of this article
    by Hari Pandey 
    Abstract: In this paper, a Bit Mask Oriented Genetic Algorithm (BMOGA) is proposed for Grammatical Inference (GI). GI is techniques to infer a context free grammar from a set of positive and negative samples. The BMOGA combines the traditional genetic algorithm, which has a powerful global exploration capability, with a Bit Mask Oriented Data Structure (BMODS) and Boolean based procedure (uses Boolean operators) that can exploit an optimum offspring. The evolutionary operations are performed in two phases: mask-fill for both crossover and mutation and then a Boolean operator based procedure. A vector function is utilized with arguments such as crossover, mutation masks and a couple of parents strings. The arguments crossover and mutation mask helps in replacing various mating rules and therefore no strict rules are to be designed to select an appropriate crossover mechanism. An extensive parameter tuning is done that makes the proposed algorithm more robust, statistically sound, and quickly convergent. The proposed BMOGA is effectively applied over the context free as well as regular languages of varying complexities. The computational experiments show that the proposed BMOGA finds optimal or close-to-optimal grammar with the best fitness value. The Boolean operators introduce diversity in the population that helps in exploring the search space adequately. First, we evaluate the performance of the BMOGA against three algorithms: the genetic algorithm, particle swarm optimization and simulated annealing. Then, the BMOGA is tested with two different offspring generation algorithms: random offspring generation and elite mating pool approach. Statistical tests are conducted that indicate the superiority of the proposed algorithm over others. Overall, a genetic algorithm based tool is developed for the GI, which greatly improves the results, robustness and alleviate premature convergence.
    Keywords: Bit-Masking Oriented Data Structure; Context Free Grammar; Genetic Algorithm; Grammar Inference; Learning System.

  • Labor division in swarm intelligence for allocation problems: A survey   Order a copy of this article
    by Renbin Xiao, Yingcong Wang 
    Abstract: Labor division in swarm intelligence is a kind of swarm behavior widely found in social insects and it provides a flexible task allocation method which is enlightening to solve the allocation problems in dynamic environment. This paper presents a comprehensive survey of labor division in swarm intelligence for allocation problems. At first, labor division in swarm intelligence is introduced and stated from the aspects of phenomena, patterns, characteristics and mechanisms, and then four kinds of models of labor division, viz., group dynamics model, response threshold model, activator-inhibitor model and individual sorting model, are discussed in detail. Based on the contrastive analysis and typical applications of these four models, the allocation problems are divided into continuous allocation problems and discrete allocation problems, and the key points of applying the stimulus-response way and the activation-inhibition way to this two types of problems are analyzed. Furthermore, the research of labor division in swarm intelligence in optimization algorithms and its advantages are discussed. Finally, some perspectives on the development trends of labor division in swarm intelligence are given as the concluding remarks of this paper.
    Keywords: swarm intelligence; labor division; task allocation; flexibility; allocation problem.

  • Hybrid Cuckoo Search Algorithm with Covariance Matrix Adaption Evolution Strategy for Global optimisation Problem   Order a copy of this article
    by Xin Zhang, Xiangtao Li, Minghao Yin 
    Abstract: Cuckoo search algorithm (CS) is an efficient bio-inspired algorithm and has been studied on global optimisation problems extensively. It is expert in solving complicated functions but converges slowly. Another optimisation algorithm, covariance matrix adaption evolution strategy (CMA_ES) can speed up the convergence rate via self-adaptation of the mutation distribution and cumulation of the evolution path, whereas it performs badly in complex functions. Therefore, in this paper, we devise a hybridization of CS and CMA_ES and name it CS_CMA, to enhance the convergence speed and performance for the different optimisation problems. An evolved population is initialised at the beginning of iteration, using the information of previous evolution. During the whole evolution process, self-adaptive parameter adjustments are employed through the successful parameter values. To validate the performance of CS_CMA, comparative experiments are conducted based on seven high-dimensional benchmark functions provided for CEC 2008 and an engineering optimisation problem chosen from CEC 2011, and the computational results demonstrate that CS_CMA outperforms other competitor algorithms.
    Keywords: Cuckoo Search; Covariance Matrix Adaptation Evolutionary Strategy; Global optimisation; Self-adaptive method; Cumulation.
    DOI: 10.1504/IJBIC.2017.10004358
  • Remote Sensing Image Fusion based on Shearlet and Genetic Algorithm   Order a copy of this article
    by Qiguang Miao, Ruyi Liu, Yining Quan, Jianfeng Song 
    Abstract: Image fusion is to combine information from two or more images of a scene into a single composite image, which will produce more information for visual perception or computer processing. In recent years, many algorithms have been developed, but there exist information loss and image distortion. To produce a satisfactory fusion result, an image fusion algorithm based on shearlet and genetic algorithm is proposed. As one of the Multiscale Geometric Analysis (MGA) tools, shearlet is equipped with a rich mathematical structure which is associated to a multi-resolution analysis. Genetic algorithm (GA) is an optimization algorithm. So GA can be used to image fusion where parameter optimization is required. Firstly, shearlet is performed on each input image to obtain their low-pass and high-pass coefficients. And then, GA is used to optimize the weighted factors in the fusion rule. Finally, the fused image is obtained by inverse shearlet transform. Experimental results have demonstrated that our method could acquire better fusion quality than any other methods.
    Keywords: Image Fusion; Shearlet; Genetic Algorithm; Fitness Function; MultiscalernGeometric Analysis; Fusion Rule.

  • Ant Colonial Based Approach for the Minimal Full Trie Problem   Order a copy of this article
    by Meng Zhang 
    Abstract: A trie of a set of strings P is a digital search tree in which leaves correspond to a string in P. Reducing the storage requirement of tires is one of the major problems of trie. One method for reducing the space required is to reorder symbol testing. Unfortunately, the problem of finding an ordering which guarantees a minimum-size trie is NP-complete. We present a trie minimization algorithm using Ant Colony Optimization (ACO). We introduce the dynamic graph and the ant teams to ACO that works out the solution by a group of cooperating ants. Experiments show that our approach generates tries that are small than that generated by the greedy method.
    Keywords: Ant Colony; Trie; Optimaiztion.

  • Bio-Inspired Reaction Diffusion System Applied to Image Restoration   Order a copy of this article
    by Nour Eddine Alaa, Mariam Zirhem 
    Abstract: In this paper, we propose a new model of nonlinear and anisotropic reaction diffusion system applied to image restoration and to contrast enhancement. This model is based on a system of partial differential equations of type Fitzhugh-Nagumo. We apply the previous algorithm and the proposed one to realistic images and we confi rm the robustness and the performance of our algorithm through a number of experimental results that indicates that it is very efficient for removing noise, enhance image contrast and preserve the edges.
    Keywords: Nonlinear anisotropic diffusion; image restoration; Fitzhugh-Nagumo; reaction diffusion system.

  • A Firefly inspired Game Dissemination and QoS based Priority Pricing Strategy for Online Social Network Games   Order a copy of this article
    by Ebin Deni Raj, Dhinesh Babu L.D 
    Abstract: With almost all educated individuals having access to computing cum communication gadgets like mobiles, tablets, pcs, and laptops, Online Social Networks (OSNs) have become the default means of networking among majority of individuals. OSNs have become an inseparable part of daily lives attracting more than one third of the current world population. Majority of the users enjoy the entertainment aspects of OSNs like gaming with friends from different geographic locations. Social gaming has spawned a whole new sub culture which helps users to discover and build connections with other users. Game development companies constantly try to publicize and attract new users using OSNs to enhance their revenues. In this context, we propose a new firefly inspired strategy to spread and disseminate games in OSNs and assist the gaming companies to decrease the acceptance- discontinuance anomaly. Collective behaviour in online social network is closely related to swarm intelligence techniques. We have also proposed a rewarding and efficient Firefly inspired QoS based priority pricing model that will attract more users to play online games while using online social networks, thereby, enhancing the profits of the service providers and game developers.
    Keywords: Online social networks; Firefly algorithm; Acceptance-discontinuance anomaly; priority pricing; OSN based games; cloud gaming.

  • Dynamic Data Clustering by Combining Improved Discrete Artificial Bee Colony Algorithm with Fuzzy Logic   Order a copy of this article
    by Ehsan Amiri, Mohammad Naderi Dehkordi 
    Abstract: Data clustering is a method of partitioning data into different groups pursuant to some similarity or dissimilarity measure. Nowadays, several different technics are invented and introduced for data clustering such as heuristics and meta-heuristics. Many clustering algorithms fail when dealing with multi-dimensional data. In this research, we proposed an innovative fuzzy method with improved discrete artificial bee colony (ID is ABC) for data clustering called FID is ABC. The D is ABC is a new version of Artificial Bee Colony (ABC) that first introduced to sort out the uncapacitated facility location (UFLP) problem and improved by the efficient genetic selection to solve dynamic clustering problem. The performance of our algorithm is evaluated and compared with some well-known algorithms. The results show that our algorithm has better performance in comparison with them.
    Keywords: Data clustering; Artificial Bee Colony (ABC) Algorithm; dataset; Fuzzy logic; Artificial Intelligence.

  • Iterative Sequential Bat Algorithm for Free-Form Rational Bezier Surface Reconstruction   Order a copy of this article
    by Andres Iglesias, Akemi Galvez, Marta Collantes 
    Abstract: Surface reconstruction is a very important research topic with outstanding applications in many areas: CAD/CAM (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping, biomedical engineering (customized prosthesis, medical implants), medical imaging (computer tomography, magnetic resonance), and many others. A classical approach in the field is to consider free-form polynomial surfaces, such as B
    Keywords: Surface reconstruction; free-form shapes; rational Bézier surface; reverse engineering; bat algorithm.

  • Symbiotic organisms search algorithm for different economic load dispatch problems   Order a copy of this article
    by Dimitrios Gonidakis 
    Abstract: Economic load dispatch (ELD) is an important topic in engineering management as it is associated with the efficient operation of an electric power generating system. The aim of ELD is to determine the generation dispatch among the thermal units such that the operating cost is minimised. This study presents a novel nature-inspired optimisation method called symbiotic organisms search (SOS) to solve various types of ELD problems. SOS imitates the interaction strategies adopted by organisms to survive in an ecosystem. The proposed method is applied to six different test systems in order to verify its effectiveness and robustness. The simulation results of the proposed SOS algorithm confirm its superiority over other successful optimisation approaches reported in recent literature.
    Keywords: symbiotic organisms search; metaheuristics; engineering optimisation; economic load dispatch; valve point loading.

    by Dhalia Sweetlin J, H. Khanna Nehemiah, Kannan Arputharaj 
    Abstract: In this work, a Computer-aided diagnosis (CAD) system to improve the diagnostic accuracy and consistency in image interpretation of pulmonary tuberculosis is proposed. The lung fields are segmented using region growing and edge reconstruction algorithms. Texture features are extracted from the diseased regions manifested as consolidations, cavitations and nodular opacities. A wrapper approach that combines cuckoo search optimization and one-against-all SVM classifier is used to select optimal feature subset. Cuckoo search algorithm is implemented first using entropy and second without using entropy measure. Training is done with the selected features using one-against-all (SVM) classifier. Among the 98 features extracted from the diseased regions, 47 features are selected with entropy measure giving 92.77% accuracy. When entropy measure is not used, 51 features are selected giving 91.89% accuracy. From the results, it is inferred that selecting appropriate features for training the classifier has an impact on the classifier performance.
    Keywords: Computer aided diagnosis; Pulmonary Tuberculosis; Tuberculosis manifestations; Binary Cuckoo Search; one-against-all SVM classification; Drug sensitive TB; Miliary TB; Cavitary TB; Nodular TB.

    by Saranya G, H. Khanna Nehemiah, Kannan Arputharaj 
    Abstract: Code smells are characterized as the structural defects in the software which indicate a poor software design and in turn makes the software hard to maintain. However, detecting and fixing the code smell in the software is a time consuming process, and it is difficult to fix manually. In this paper, an algorithm named as Hybrid Particle Swarm Optimization with Mutation (HPSOM) is used for identification of code smell by automatic generation of rules which represent the combination of metrics and threshold. Moreover, an empirical evaluation to compare HPSOM with other evolutionary approaches such as the Parallel Evolutionary Algorithm (PEA), Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimization (PSO) to detect the code smell is done. The analysis shows that the HPSOM algorithm performs better than other approaches when applied on nine open source projects, namely, JfreeChart, GanttProject, ApacheAnt 5.2, ApacheAnt 7.0, Nutch, Log4J, Lucene, Xerces-J and Rhino. HPSOM approach has achieved precision of 94% and recall of 92% on five different types of code smells namely, Blob, Data class, Spaghetti code, Functional decomposition and Feature envy.
    Keywords: Search based software engineering; Code smell; Evolutionary algorithms; Software maintenance; Software metrics; Particle Swarm Optimization; Hybrid Particle Swarm Optimization; Cohesion; Coupling; Open source software.

  • Biogeography-based Optimization with Migration Velocity for Multi-objective Optimization Problems   Order a copy of this article
    by Weian Guo, Lei Wang 
    Abstract: Biogeography-based Optimization (BBO) is a well-used nature-inspired algorithm in dealing with optimization problems. In our previous work, we have applied this algorithm to Multi-objective Optimization Problems (MOPs) and termed the algorithm Multi-objective Biogeography-based Optimization (MOBBO). However, in the design of the original migration operator, a selected emigrant is supposed to be completely replaced by an immigrant, which makes the population diversity be apt to deteriorate. Therefore, the ability to approximate the true Pareto Front will be weaken. To address this issue, in this paper, we design a velocity variable for each individual, which depicts the degree how immigrants affect emigrants. The positions of each individual which represents an candidate solutions will be affected by their velocities. In this way, emigrants and immigrators velocities are involved in one migration operator so that the population diversity maintains. We employ several classical benchmarks to compare the improved MOBBO with several other classical algorithms and the results demonstrate that the improved MOBBO with migration velocity is much competitive in addressing MOPs.
    Keywords: Biogeography-Based Optimization; Evolutionary Algorithm; Multi-Objective Optimization Problems; Migration Operator; Migration Velocity.

  • Embedded Implementation of Template Matching using Correlation and Particle Swarm Optimization
    by Yuri Tavares, Nadia Nedjah, Luiza Mourelle 
    Abstract: Template matching is an important technique used in pattern recognition. The goal is find a given pattern, from a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearson’s Correlation Coefficient (PCC) is widely used. This coefficient is calculated for each of the image pixels, which entails a computationally very expensive operation. This paper proposes the implementation of Template Matching using the PCC based method together with Particle Swarm Optimization as an embedded system. This approach allows for a great versatility to use this kind of system in portable equipment. The results indicate that PSO is up to 131× faster than the brute force exhausted search. So, the thus obtained co-design with PCC computation implemented in hardware, while the PSO process in software, is a viable way to achieve real time template matching, which is a pre-requisite in real-word applications.
    Keywords: Embedded systems; co-design; particle swarm optimization; template matching; correlation; tracking.

Special Issue on: New Challenges in Bio-inspired Algorithms in Complex and Distributed Problems

  • Communication Overlay for Communities of Collaborative Agents in Smart Grid Domains   Order a copy of this article
    by Marco Scialdone, Luca Tasquier, Rocco Aversa, Salvatore Venticinque 
    Abstract: The increasing demand for energy has stimulated the formulation of plans aiming at shifting to renewable energies, not only to save money, but also for the responsibility that the present population has towards future generations. Under this aspect, ICT based solutions can be used to enable energy and money saving not only for a single building, but for the whole community of a neighbourhood. rnIn this paper we present an agent-based framework that allows the management and negotiation of decentralized energy production. We propose a P2P overlay network whereby the agents distributed within the community can negotiate the energy in a transparent way with respect to the different technologies used, thus ensuring scalability of the overall architecture. We analyze the performance of the proposed solution in different scenarios by using a set of testbeds developed to evaluate the prototypal implementation of the framework and of the communication network.
    Keywords: multi-agent system;smart cities;decentralized energy negotiation;P2P overlay network.
    DOI: 10.1504/IJBIC.2017.10004417
  • Multi-Leader Migrating Birds Optimization: A novel nature-inspired metaheuristic for combinatorial problems   Order a copy of this article
    by Eduardo Lalla-Ruiz, Jesica De Armas, Christopher Exposito-Izquierdo 
    Abstract: In this paper, we present Multi-Leader Migrating Birds Optimization (MMBO). This algorithm is a nature-inspired population-based approach that exploits the concepts of self-organization, cooperation, and distribution of migrating birds. For this purpose, the individuals maintain a well-defined relationship scheme. In this regard, each individual modifies a solution of the problem at hand through a set of operators defined by the user. The individuals cooperate among themselves during the search process by sharing information about the explored search space. Moreover, we assess the performance of our algorithm on the Quadratic Assignment Problem (QAP) due to the large number and heterogeneous characteristics of its application fields. The computational results indicate that MMBO is highly competitive when solving this problem and provides new best solutions for the QAP applied to Printed Circuit Board Problem.
    Keywords: Nature-inspired; Metaheuristic; Multi-leader Migrating Birds Optimization.
    DOI: 10.1504/IJBIC.2017.10004319
  • A Meta-heuristic Learning Approach for the Non-Intrusive Detection of Impersonation Attacks in Social Networks   Order a copy of this article
    by Esther Villar-Rodriguez, Javier Del Ser, Sergio Gil-Lopez, Miren Nekane Bilbao, Sancho Salcedo-Sanz 
    Abstract: Cyber attacks have recently gained momentum in the research community as a sharply concerning phenomenon further ignited by the proliferation of social networks, which unfold a variety of ways for cybercriminals to access compromised information of their users. The general lack of awareness regarding both these risks and the consequences of an eventual security breach ends up with large amounts of exposed data susceptible to be stolen and/or exploited with malevolent and fraudulent objectives (e.g. phishing or bullying). This paper gravitates on impersonation attacks, whose motivation may go beyond economic interests of the attacker towards getting unauthorized access to information and contacts, as often occurs between teenagers and early users of social platforms. This manuscript proposes a meta-heuristically optimized unsupervised learning strategy as the algorithmic core of a privacy-aware detection system that relies exclusively on connection time features to detect evidences of an impersonation attack. The proposed scheme hinges on the K-Means clustering approach applied to a set of time features specially tailored to characterize the usage of users, which are weighted prior to the clustering under a detection performance maximization approach. The obtained experimental results are promising and shed light on the potentiality of the proposed methodology for its practical application to real social networks.
    Keywords: Impersonation; Identity theft; Social Networks; K-Means; Harmony Search.
    DOI: 10.1504/IJBIC.2017.10004416
  • Solving Strategy Board Games using a CSP-based ACO Approach   Order a copy of this article
    by Antonio Gonzalez-Pardo, Javier Del Ser, David Camacho 
    Abstract: In the last years, there have been a huge increase in the number of research contributions that use games and video-games as an application domain for testing different artificial intelligence algorithms. Some of these problems can be represented as a Constraint-Satisfaction Problem (CSP), and heuristics algorithms (such as Ant Colony Optimization) can be used due to the complexity of the modelled problems. This paper presents a comparative study of the performance of a novel ACO model for CSP-based board games. In this work, two different Oblivion Rate meta-heuristics for controlling the number of pheromones created in the model have been created. Experimental results reveal that both meta-heuristics reduce considerably the number of pheromones produced in the system without affecting the quality of the solutions in terms of average optimality.
    Keywords: Ant Colony Optimization; Pheromone Control; Oblivion Rate; Strategy Board Games.
    DOI: 10.1504/IJBIC.2017.10004320
  • Extending the SACOC algorithm through the Nystrom method for Dense Manifold Data Analysis   Order a copy of this article
    by Hector Menendez, Fernando Otero, David Camacho 
    Abstract: Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context. Using a dataset as a starting point, clustering techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are one of the main used methodologies in this area, are sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the cluster selection, in particular for dense datasets. Dense datasets are featured by areas of higher density, where there are significantly more data instances than in the rest of the search space. This paper presents an extension of a previous algorithm named Spectral-based Ant Colony Optimization Clustering (SACOC), a spectral-based clustering methodology used for manifold identification. This work focuses on improving the SACOC algorithm through the Nystrom extension in order to deal with dense data problems. We evaluated the performance of the proposed approach, called SACON, comparing it against online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several benchmark datasets.
    Keywords: Ant Colony Optimization; Clustering; Data Mining; Machine Learning; Spectral; Nystr"{o}m; SACON; SACOC.
    DOI: 10.1504/IJBIC.2017.10004321
  • A distributed combinatorial optimisation heuristic for the scheduling of energy resources represented by self-interested agents   Order a copy of this article
    by Christian Hinrichs, Michael Sonnenschein 
    Abstract: The aggregation of controllable distributed energy resources (DER) to virtual power plants (VPPs) forms a possible integration path for DER in future energy systems. The authors present a fully distributed scheduling heuristic for VPPs. The approach is realised by representing each participant of a VPP by a self-interested agent. Both the global, operator-driven scheduling objective of a VPP as well as arbitrary individual local objectives of the agents are integrated efficiently in a fully distributed coordination paradigm. Convergence and termination of the heuristic are proven in the presence of unreliable environments, e.g. with communication delays.
    Keywords: self-organisation; heuristic; distributed optimisation; combinatorial optimisation; scheduling; energy resources; energy markets; day-ahead; smart grid; virtual power plants; multi agent systems; self-interested agents; convergence; termination; proof.
    DOI: 10.1504/IJBIC.2017.10004322
  • Robust Variant of Artificial Bee Colony (JA-ABC4b) Algorithm   Order a copy of this article
    by Noorazliza Sulaiman, Junita Mohamad-Saleh, Abdul Gani Abro 
    Abstract: The simplicity and robustness of the Artificial Bee Colony (ABC) algorithm has attracted the attention of optimization researchers. Although ABC has fewer tuned parameters, making it an easy-to-use tool, it has shown better performance than other prominent optimization algorithms such as differential evolution (DE), evolutionary algorithms (EA) and particle swarm optimization (PSO) algorithms at solving optimization problems. Despite these advantages, researchers have found that the standard ABC actually suffers from slow convergence on unimodal functions and is often trapped in local minima of multimodal functions. Most problematically, it does not balance the exploitation and exploration stages, leading to various inefficiencies in terms of capability. This paper presents a new ABC variant referred to as JA-ABC4b, which has been formulated to balance exploitation and exploration in order to boost optimization performance. JA-ABC4b has been experimentally tested on 27 benchmark functions and economic environmental dispatch (EED) problems. The results have revealed a robust performance of JA-ABC4b in comparison to other existing ABC variants and other optimization algorithms.
    Keywords: Artificial intelligence; artificial bee colony; swarm intelligence-based algorithm; optimization algorithm; economic environmental dispatch.
    DOI: 10.1504/IJBIC.2017.10004418
  • Network size and topology impact on trust-based ranking   Order a copy of this article
    by Alessandro Longheu, Vincenza Carchiolo, Michele Malgeri, Giuseppe Mangioni 
    Abstract: The participation to virtual contexts, where individuals exchange each other personal information has been increased during the last decade.rnTrust is often used in such a scenario as a mechanism to establish reliable relationships. rnIn addition, trust is often used to rank nodes and the higher the rank the more preferred that node will be. Since this requires some cost, a trade-off between the desired position and the related cost should be achieved.rnIn this paper, we analyze the rank-vs-cost function in trust networks, considering different topology and number of nodes.rnResults show that for the first position the effort is independent on network size for all topologies but scale-free, where costs increases strongly.rnMoreover, if we want a good position, i.e. within the first 5\% or 10\% of nodes, rnall networks exhibit similar behaviour for any sizes, therefore such compromise is advisable in most cases (including scale-free).
    Keywords: Trust; Complex networks; Scale-free networks; Random networks; Ranking.
    DOI: 10.1504/IJBIC.2017.10004323

Special Issue on: Applied Metaheuristics for Addressing Big Data Problems

  • TTPA: A Two Tiers PSO Architecture for Dimensionality Reduction   Order a copy of this article
    by Shikha Agarwal, Prabhat Ranjan 
    Abstract: Particle swarm optimization (PSO) is a popular nature inspired computing method due to its fast & accurate performance, exploration & exploitation capability, cognitive & social behaviour and has fewer parameters to adjust. Recently an improved binary PSO (IBPSO) was proposed by Chuang et. al. to avoid getting trapped in local optimum and they have shown that it outperforms all other variants of PSO. Even though many variants of PSO exists independently to improve the performance of PSO, to escape from local optimum and to deal with dimensionality reduction, there still needs an integrated approach to handle it. Hence in this paper two tiers PSO architecture (TTPA) is proposed to find the maximum classification accuracy with minimum number of selected features. The proposed method is used to classify nine benchmarking gene expression data sets. The results show the merits of TTPA.
    Keywords: Dimensionality Reduction; Binary Particle Swarm Optimization; FeaturernSelection; Microarray Gene Expression Profile Data.

Special Issue on: CBIC & LA-CCI 2015 Swarm Intelligence Algorithms and Applications

  • Population-based Variable Neighborhood Search Algorithm Applied to Unconstrained Continuous Optimization   Order a copy of this article
    by Rafael Parpinelli, Wesklei Migliorini 
    Abstract: This work presents a population-based Variable Neighborhood Search approach for unconstrained continuous optimization, called PRVNS. The main contributions of the proposed algorithm are to evolve a population of individuals (i.e., candidate solutions) and to allow each individual adapts its own neighborhood search area accordingly to its performance. The adaptive amplitude control allows individuals to autonomously exploit and explore promising regions in the search space. Several unconstrained continuous benchmark functions with a high number of dimensions (d=250) are used to evaluate the algorithm's performance. The PRVNS results are compared with the results obtained by some well known population-based approaches: Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). Also, the standard VNS algorithm is considered in the experiments. The results and analyses suggest that the PRVNS approach is a promising and competitive algorithm for unconstrained continuous optimization.
    Keywords: Unconstrained Continuous Optimization; Population-based Algorithm; Variable Neighborhood Search; Meta-heuristic.
    DOI: 10.1504/IJBIC.2017.10004327
  • An Alternative Approach for Particle Swarm Optimization Using Serendipity   Order a copy of this article
    by Fabio Paiva, Jose Costa, Claudio Silva 
    Abstract: In study of metaheuristic techniques, it is very common to deal with a problem known as premature convergence. This problem is widely studied in the genetic algorithms context, but also has been observed in Swarm Intelligence methods such as Particle Swarm Optimization (PSO). Most approaches to the problem consider the generation and/or positioning of individuals in the search space randomly. This paper approaches the issue using the concept of serendipity and its adaptation in this new context. Several techniques that implement serendipity were evaluated in order to develop a PSO variant based on this concept. The results were compared with the traditional PSO considering the quality of the solutions and the ability to find global optimum. The prototype also was compared with a PSO variant. It showed promising results related to the criteria mentioned above, however there is the need for additional adjustments to decrease the runtime.
    Keywords: particle swarm optimization; serendipity; swarm intelligence; global optimisation; bio-inspired computation; metaheuristic.
    DOI: 10.1504/IJBIC.2017.10004328
  • Feature Selection based on Binary Particle Swarm Optimization and Neural Networks for Pathological Voice Detection   Order a copy of this article
    by Suzete Correia, Taciana Souza, Vinicius Vieira, Micael Souza, Silvana Costa, Washington Costa 
    Abstract: The voice quality may be affected by laryngeal pathologies. Acoustic analysis employing digital signal processing techniques have been used to detect the presence of laryngeal diseases. The choice regarding the appropriated features of voice signals which are really relevant to discriminate healthy from pathological voices is still a challenge. In this work, 52 Haralick texture features, extracted from two-dimensional wavelet coefficients of speech signals from recurrence plots (RP) pathologies are used for pathological voice discrimination. Here, three pathologies are considered for analysis: vocal fold paralysis, edema and nodules. For feature selection, a binary Particle Swarm Optimization (PSO) algorithm using Multilayer Perceptron (MLP) neural network with cross validation is employed. The adopted fitness function is based on the maxima average accuracy rate. Statistical tests for individual measures were made and their results show statistical significance for several employed measures. The measures were combined and the more relevant ones based on the highest accuracy were selected by the PSO. The comparison with and without PSO by applying the statistical test of mean difference showed that the PSO use increased the accuracy rates. Furthermore, the PSO use reduced the amount of features for almost half of all initially used.
    Keywords: Laryngel pathologies detection; Acoustic Analysis; Recurrence Plots; Haralick texture features; Particle Swarm Optimization; Wavelet Transform.
    DOI: 10.1504/IJBIC.2017.10004331
  • A Memetic Algorithm for Power System Damping Controllers Design   Order a copy of this article
    by Wesley Peres, Valceres V. R. Silva, Francisco C. R. Coelho, Ivo C. Silva Junior, João Alberto Passos Filho 
    Abstract: This paper presents a hybrid algorithm for robust and coordinated design of power system stabilisers. Power system stabilisers are controllers installed on synchronous generators for excitation control in order to damp power system oscillations. The tuning procedure (gain and phase compensation stage) is cast as an optimisation problem which aims at maximising the damping coefficients in closed-loop operation. Robustness is dealt with by using multiple operating scenarios. For the optimisation problem solution, the bio-inspired Bat Algorithm is combined with the Steepest Descent Method for local search capability enhancement. The proposed algorithm is applied to benchmark systems for validation.
    Keywords: bat algorithm; steepest descent method; power system stabilisers; small-signal stability.
    DOI: 10.1504/IJBIC.2017.10004332
  • Using the metaheuristic methods for real-time optimization of dynamic school bus routing problem and an application   Order a copy of this article
    by Tuncay Yigit, Ozkan Unsal, Omer Deperlioglu 
    Abstract: The Vehicle Routing Problem (VRP) is an optimization issue that has been studied for more than 50 years with its numerous subfields. The optimization of VRP over distribution and transportation systems leads to significant gains in cost and time. There are many metaheuristic methods developed for the solution of the problem; and it was observed that metaheuristic methods prove to produce more successful results compared to common heuristic methods. In this study, a mobile-supported visual application was developed using Ant Colony Optimization (ACO) and Genetic Algorithm (GA), which are among the metaheuristic methods for the dynamic school bus routing problem (DSBRP), one of the sub-problems of VRP. The ACO and GA methods were utilized via the application for bus routes of a school located in the province of Ankara and the performance of these methods were compared through the obtained results. It was observed that time and distance values of the routes of current school bus routes may be improved by these two methods.
    Keywords: Metaheuristic methods; Real-time optimization; Dynamic School Bus Routing Problem; Ant Colony Optimization; Genetic Algorithm.
    DOI: 10.1504/IJBIC.2017.10004333

Special Issue on: Recent Advances in Metaheuristics and Swarm Intelligence for Software Testing, Quality and Applications

  • A Novel Artificial Bee Colony Optimizer with Dynamic Population Size for Multi-level Threshold Image Segmentation   Order a copy of this article
    by Lianbo Ma, Xingwei Wang, Hai Shen 
    Abstract: Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimizer is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.
    Keywords: Artificial bee colony algorithm; Population varying; Image segmentation.
    DOI: 10.1504/IJBIC.2016.10004298