International Journal of Bio-Inspired Computation (34 papers in press)
A Novel Artificial Bee Colony Optimizer with Dynamic Population Size for Multi-level Threshold Image Segmentation
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.
Multi-swarm Cooperative Multi-objective Bacterial Foraging Optimization
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.
Hybrid Cuckoo Search Algorithm with Covariance Matrix Adaption Evolution Strategy for Global optimisation Problem
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.
TTPA: A Two Tiers PSO Architecture for Dimensionality Reduction
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.
COMPUTER AIDED DIAGNOSIS OF DRUG SENSITIVE PULMONARY TUBERCULOSIS WITH CAVITIES, CONSOLIDATIONS AND NODULAR MANIFESTATIONS ON LUNG CT IMAGES
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.
Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem
by Sanyou Zeng, Ruwang Jiao, Changhe Li, Rui Wang
Abstract: A constrained optimisation problem (COP) is solved by solving an equivalent dynamic loosely-constrained multiobjective optimisation problem in this paper. Two strategies are considered. i) An additional objective (constrained-violation objective) is introduced to obtain a twoobjective optimisation problem. This provides a framework for adopting multi-objective techniques to solve the COP. ii) A dynamic constraint boundary is introduced to obtain an equivalent dynamic loosely-constrained multiobjective optimisation problem since a broad boundary is gradually slightly reduced to the original constraint boundary. This suggests that an dynamic constrained multiobjective evolutionary algorithm (DCMOEA) can performs as effective as that of a multiobjective evolutionary algorithm (MOEA) in solving an unconstrained multiobjective optimisation problem. The idea is implemented into three major types of MOEAs, i.e., Pareto ranking based method, decomposition based method, preference-inspired co-evolutionary method. These three instantiations are tested on two sets of benchmark problems. Experimental results show that they are better than or competitive to two state-of-the-art constraint optimizers, especially for the problems with high dimensions.
Keywords: evolutionary algorithm; constrained optimisation; multi-objective optimisation; dynamic optimisation.
Swarm Intelligent Based Congestion Management Using Optimal Rescheduling of Generators
by Surender Reddy Salkuti, Wajid S.A.
Abstract: Congestion Management (CM) refers to the controlling of transmission system such that the power transfer/flow limits are observed. In the restructured electrical system, the challenges of CM for the System Operator (SO) is to maintain the desired level of system reliability and security in the short and long terms, while improving the market/system efficiency. In this paper, the CM problem is tackled by using the centralized optimization i.e., optimal rescheduling of generators, which in turn is solved by using the Swarm intelligent techniques. Here, the CM problem is solved by using the Particle Swarm Optimization (PSO), Fitness Distance Ratio PSO (FDR-PSO) and Fuzzy Adaptive-PSO (FA-PSO). First, the generators are selected based on sensitivity to the over-loaded transmission line, and then these generators are rescheduled to remove the congestion in the transmission line. The suitability and effectiveness of the proposed CM approach is examined on the standard IEEE 30 bus and practical Indian 75 bus systems.
Keywords: Congestion management; Generation rescheduling; Optimal power flow; Generator sensitivity; Evolutionary algorithms; Particle swarm optimization.
Intelligent Swarm Firey Algorithm for the Prediction of China's National Electricity Consumption
by Guangfeng Zhang, Yi Chen
Abstract: China's energy consumption is the world's largest and is still rising, leading to concerns of energy shortage and environmental issues. It is, therefore, necessary to estimate the energy demand and to examine the dynamic nature of the electricity consumption. In this paper, we develop a nonlinear model of energy consumption and utilise a computational intelligence approach, specifically a swarm firefly algorithm with a variable population, to examine China's electricity consumption with historical statistical data from 1980 to 2012. Prediction based on these data using the model and the examination is verified with a bivariate sensitivity analysis, a bias analysis and a forecasting exercise, which all suggest that the national macroeconomic performance, the electricity price, the electricity consumption efficiency and the economic structure are four critical factors determining national electricity consumption. Actuate prediction of the consumption is important as it has explicit policy implications on the electricity sector development and planning for power plants.
Keywords: energy consumption; nonlinear modelling ; swarm firefly algorithm; parameters determination.
A Moving Block Sequence-based Evolutionary Algorithm for Resource-Constrained Project Scheduling Problems
by Xingxing Hao, Jing Liu, Xiaoxiao Yuan, Xianglong Tang, Zhangtao Li
Abstract: In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, therefore, it can be modeled as a rectangle block whose height represents the resource demand and width the duration. Naturally, a project that consists of N activities can be represented as the permutation of N blocks that satisfy the precedence constraints among activities. To decode an MBS to a valid schedule, four move modes are designed according to the situations that how every block can be moved from its initial position to an appropriate location that can minimize the makespan of the project. Based on MBS, the multiagent evolutionary algorithm (MAEA) is used to solve RCPSPs. The proposed algorithm is labeled as MBSMAEA-RCPSP, and by comparing with several state-of-the-art algorithms on benchmark J30, J60, J90 and J120, the effectiveness of MBSMAEA-RCPSP is clearly illustrated.
Keywords: Moving block sequence; Resource-constrained project scheduling problems; Evolutionary algorithms.
Cloud service composition using an inverted ant colony optimization algorithm
by Saied Asghari, Nima Jafari Navimipour
Abstract: In recent years, clouds are becoming an important platform for scientific applications. Service composition is a growing approach that increases the number of applications of cloud computing by reusing attractive services. However, more available approaches focus on producing composite services from a single cloud, limiting the benefits derived from other clouds. Furthermore, in many traditional service composition methods, there is a key problem called load balancing that was inefficient among cloud servers. Therefore, this paper proposes the inverted ant colony optimization (IACO) algorithm, a variation of the basic ant colony optimization (ACO) algorithm, to solve this problem. This method inverts its logic by converting the effect of pheromone on the selected path by ants in order to improve load balancing among cloud servers. In this method, ants begin to traverse the graph from the start node and each ant selects the best node for moving, then other ants may not follow the track travelled by the previous ants. We evaluate the performance of the proposed method in comparison with the ACO, greedy and COM2 algorithms in terms of the obtained optimal cloud composition, load balancing, waiting time, cost and execution time. The results show that the proposed method improves load balancing, reduces waiting time and cost that are the advantages of the proposed method and also increases execution time that is a disadvantage of the proposed method.
Keywords: Cloud computing; Inverted ant colony; Service composition; Load balancing; Optimal cloud composition.
Markov Approach for Quantifying the Software Code Coverage Using Genetic Algorithm in Software Testing
by Sujatha Ramalingam, Boopathi Muthusamy, Senthil Kumar C, Narasimman S, Rajan A
Abstract: Markov Chain approach to quantify the coverage of dd-graph representingrnthe software code using genetic algorithm (GA) is presented in this paper. Initially the ddgraph is captured from the control flow graph. In this technique, test software code coverage is carried out by applying GA through sufficient number of feasible linearly independent paths. These paths have been decided in a software code depending on computational uses and predicate uses. Automatic test cases have been produced for the three mixed data type variables namely, integer, float, Boolean and GA is applied. Transition Probability of the Markov Chain is attained from gcov coverage analysis tool of the initial test suite. Fitness function of GA is measured using path coverage metric; as the product of node coverage and TPM values. Highest fitness value represent the most critical paths among these independent paths with aim to increase testing efficiency of the software code.
Keywords: Software testing; Test adequacy; Cyclomatic complexity; Markov Chain; dd-rngraph; Genetic Algorithm.
Solving many-objective optimization problems by an improved particle swarm optimization approach and a normalized penalty method
by Dexuan Zou, Fei Wang, Nannan Yu, Xiangyong Kong
Abstract: The problem with more than three objectives is commonly known as many-objective optimization problem (MOP), and it has drawn much attention from researchers because of its big potential in the real word. In this paper, a novel modified particle swarm optimization (NMPSO) approach is presented to handle a kind of MOP called many-objective knapsack (MOK) problem. NMPSO relies on the global best particle to guide the search of all particles in each generation, which can enhance the convergence of NMPSO. Furthermore, a randomization-based mutation is adopted to overcome the premature convergence which usually occurs in the late evolutionary optimization process. In addition to many objective functions, MKP consists of several inequality constraints, and all the objective functions should be minimized under the precondition that all the inequality constraints are satisfied. A normalized penalty method (NPM) is devised to reach a compromise between objective functions and inequality constraints, which enables particles to explore solution space more precisely. In summary, the contribution of our work can be summarized in two aspects: (1) A more powerful approach called NMPSO is proposed. (2) A reasonable NPM is devised. Five improved PSOs are used to handle the MOKs with different number of objective functions and dimensions. Experimental results show that NMPSO has higher efficiency than the other four approaches. It uses the lowest computational cost, and achieves the smallest penalty function values for most MKPs.
Keywords: Many-objective optimization problem; Novel modified particle swarm optimization; Many-objective knapsack problem; Randomization-based mutation; Normalized penalty method.
Quantum Inspired Monarch Butterfly Optimization for UCAV Path Planning Navigation Problem
by Jiao-Hong Yi
Abstract: As a complicated high-dimensional optimization problem, path planning navigation problem for Uninhabited Combat Air Vehicles (UCAV) is to obtain a shortest safe flight route with different types of constrains under complicated combating environments. Monarch butterfly optimization (MBO) is a highly promising swarm intelligence algorithm. Since then, though it has successfully solved several challenging problems, MBO may be trapped into local optima sometimes. In order to improve the performance of MBO, quantum computation is firstly incorporated into the basic MBO algorithm, and a new quantum inspired MBO algorithm is then proposed, called QMBO. In QMBO, certain number of the worst butterflies are updated by quantum operators. In this paper, the UCAV path planning navigation problem is modeled into an optimization problem, and then its optimal path can be obtained by the proposed QMBO algorithm. In addition, B-Spline curves are utilized to further smoothen the obtained path and make it more feasible for UCAV. The UCAV path obtained by QMBO is compared with the basic MBO, and the experimental results show that QMBO can find much shorter path than MBO.
Keywords: Unmanned combat air vehicle; Path planning navigation; Monarch butterfly optimization; Quantum computation; B-Spline curve.
A Novel Differential Evolution Approach for Constraint Optimization
by Pooja, Praveena Chaturvedi, Pravesh Kumar, Amit Tomar
Abstract: In the present study, a modified DE framework is proposed, which is a fusion of two modifications in the parent DE: (1) self-adaptive control parameter; (2) single population structure. Both the concepts are used to modify the parent DE that improves the convergence rate without compromising on quality of the solution. While self- adaptive control parameters are used to get a good quality solution, the single population structure helps in faster convergence as reducing the memory and computational efforts. The resultant algorithm, named NDE, found by application of these concepts balances the exploration and exploitation of the parent DE algorithm. The validation of the performance of the proposed NDE algorithm is drawn on a set of benchmark test functions and is compared to several other state-of-the-arts of DE variants. Numerical results pointed out that the proposed NDE algorithm is better than or at least comparable to the parent DE algorithm.
Keywords: Differential Evolution; Control Parameters; Population Structure; Constrained Test Problems.
An Automated Screening System for Vessel Blockage Segmentation in Coronary Angiogram Images Using ANFIS Classifier
by Rajesh Kumar, K. Murugesan
Abstract: Coronary vessel blockage segmentation is the fundamental component which extracts significant features from angiogram images to detect heart disease. This paper proposes an automated method of blockage segmentation from coronary angiogram images using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The vessels are enhanced using preprocessing technique and then features are extracted from these images which are given to the ANFIS classifier. This classifier classifies the given test coronary image into either normal or abnormal. Further, the blockage is detected and segmented if the proposed system classifies the test image as abnormal. Then, the performance of the proposed system is analyzed in terms of sensitivity, specificity and accuracy with respect to ground truth images. The proposed method achieves 95.9% sensitivity, 99.9% specificity and 99.9% accuracy for blockage vessel pixel detection.
Keywords: Coronary vessel; Angiogram; Feature extraction; ANFIS classifier; Preprocessing.
Dynamic Cuckoo Search Algorithm Based on Taguchi Opposition-based Search
by Juan Li, Yuan-Xiang Li, Sha-sha Tian, Jie Zou
Abstract: The cuckoo search (CS) algorithm is a relatively new, nature-inspired intelligent algorithm that uses a whole updating and evaluation strategy to find solutions for continuous global optimization problems. Despite its efficiency and wide use, CS suffers from premature convergence and poor balance between exploitation and exploration. These issues result from interference phenomena among dimensions that arise when solving multi-dimension function optimization problems. To overcome these issues, we proposed an enhanced CS algorithm called dynamic CS with Taguchi opposition-based search (TOB-DCS) that employed two new strategies: Taguchi opposition-based search and dynamic evaluation. The Taguchi search strategy provided random generalized learning based on opposing relationships to enhance the exploration ability of the algorithm. The dynamic evaluation strategy reduced the number of function evaluations, and accelerated the convergence property. For this research, we conducted experiments on twenty-two classic benchmark functions, including unimodal, multimodal, and shifted test functions. Statistical comparisons of our experimental results showed that the proposed TOB-DCS algorithm made an appropriate trade-off between exploration and exploitation.
Keywords: cuckoo search algorithm; dynamic evaluation; orthogonal opposition-based learning; taguchi opposition-based search.
ABC_DE_FP: A Novel Hybrid Algorithm for Complex Continuous Optimization Problems
by Parul Agarwal, Shikha Mehta
Abstract: Artificial bee colony algorithm (ABC) evolved as one of the efficient swarm intelligence based algorithm in solving various global optimization problems. Though many variants of ABC have evolved, still algorithm depicts poor convergence rate in many situations. Therefore, maintaining balance between intensification and diversification of the algorithm still needs attention. In this context, a novel hybrid ABC algorithm has been developed by integrating FPA and DE in original ABC algorithm. To assess the efficacy of proposed hybrid algorithm, it is primarily compared with contemporary ABC variants such as GABC, IABC and AABC over simple benchmark problems. Thereafter, it is compared with original ABC, FPA, hybrid of ABC_FP, ABC_DE and ABC_SN over complex problems of CEC2014 for up to 100 dimensions. Results reveal that proposed algorithm outperforms its counterparts in terms of minimum error value attained and convergence speed for majority of global numerical optimization functions.
Keywords: Artificial bee colony algorithm; CEC 2014 benchmark functions; nature inspired algorithms; flower pollination algorithm; differential evolution; convergence speed.
Trust Aware Nature Inspired Optimized Routing in Clustered Wireless Sensor Networks
by Edwin Prem Kumar Gilbert, K. Baskaran, Elijah Blessing Rajsingh, M. Lydia, A. Immanuel Selvakumar
Abstract: Wireless sensor networks (WSN) consist of sensor nodes which have capabilities of sensing, computation and communication. Routing algorithms are required in a WSN when a node is unable to send a data to the base station directly. In this paper, a trust aware optimized compressed sensing based data aggregation and routing algorithm has been proposed for clustered WSN. Compressed sensing is used for data aggregation from sensor nodes with reduced overhead. Nature inspired optimization has been implemented to obtain trade-off between transmission distance, hop-count, number of transmitted message and most trusted path using artificial bee colony algorithm, ant colony optimization, differential evolution, firefly algorithm and particle swarm optimization. Trust based reconstruction of the compressed data is done at the base station in the presence of malicious nodes.
Keywords: artificial bee colony algorithm; ant colony optimization; differential evolution; firefly algorithm; multi-objective optimization; particle swarm optimization; trust management.
RoughPSO: Rough set-based Particle Swarm Optimization
by Jiancong Fan, Yang Li, Leiyu Tang, Gengkun Wu
Abstract: Particle swarm optimization (PSO) is an optimization algorithm based on stochastic search technique. PSO has many similar characteristics with evolutionary computation such as Genetic Algorithms (GA). Unlike GA, PSO has no evolution operators. In PSO, the particles (potential solutions) fly through the solution space by following the current optimum particles. However, PSO is easy to converge to a local optimum because the search process is stochastic. Rough set, in computer science, is a formal approximation of a conventional set in terms of a pair of sets. Rough set gives the lower and the upper approximation of the original set and is always used to deal with those uncertainty problems. In this paper, the properties of rough set theory are used to improve the local convergence problems in PSO, thereby an algorithm RoughPSO is proposed. RoughPSO utilizes the lower- and upper-approximation sets of rough set to obtain the membership values. These values is then used to update the velocity and position of each particle. RoughPSO is applied for function optimization and classification in machine learning. Empirical study shows that RoughPSO not only can solve the convergence to a local optimum, but also obtains higher classification accuracy rates on some datasets than those PSO-based classification algorithms.
Keywords: Particle Swarm Optimization; Rough Set; Computational Intelligence; Classification.
A modified harmony search algorithm applied to capacitor placement of radial distribution networks considering voltage stability index
by A.R. Askarzadeh, Meysam Montazeri, Leandro Coelho
Abstract: In power system, capacitor placement of distribution networks is a complex combinatorial optimization problem. In this paper, in order to effectively solve optimal capacitor placement (OCP), an aggregate harmony search (AHS) optimizer, a stochastic population-based metaheuristic algorithm, is proposed which introduces a new pitch adjustment mechanism for harmony search (HS). The objective function contains three goals: (1) minimization of the power losses, (2) minimization of the capacitor installation cost and (3) improvement of the voltage stability index at the weakest bus. On two case studies, 23 kV nine-section feeder and 33-bus radial system, simulation results show that the proposed AHS not only finds more accurate results than the other investigated HS variants and particle swarm optimization (PSO) techniques but also has the best robustness. Promising performance of AHS makes this technique as an efficient alternative to solve capacitor placement problem.
Keywords: Capacitor allocation; power loss; voltage stability index; aggregate harmony search.
Cooperative co-evolution with improved differential grouping method for large-scale global optimization
by Zhang Fuxing
Abstract: The cooperative co-evolution (CC) framework has been shown to be effective for solving large-scale global optimization (LSGO) problems. However, the performance of algorithms based on the CC framework is often influenced by the selected variable grouping method, i.e., how variables are grouped into different sub-components. This study proposes an alternative variable grouping strategy, namely-based differential grouping (ε-DG). TheDG strategy can identify both direct and indirect interactions between variables. Moreover, a simple yet effective method is introduced into the ε-DG strategy to identify the calculation error that is detrimental to variable grouping in the DG method. The effectiveness of the ε-DG method is demonstrated by comparing it with the DG strategy on the CEC 2010 LSGO benchmarks. Experimental results show that our method performs better in terms of the grouping accuracy. The algorithm derived by incorporating ε-DG into a CC-based differential evolution algorithm shows good performance on the 2010 LSGO benchmarks.
Keywords: large-scale global optimization (LSGO); variable grouping strategy; differential grouping; differential evolution; evolutionary algorithms.
Network optimisation design of Hazmat based on multi-objective genetic algorithm under the uncertain environment
by Changxi Ma
Abstract: To avoid the hazardous material (Hazmat) transportation accidents, it is necessary to design the Hazmat transportation network in advance. Due to the uncertainty of risks and time during the Hazmat transportation, the paper studies the optimal network design method under the uncertain environment. The transportation scenario is divided into two types including single-vehicle centralized service and multi-vehicle coordinated service. The opportunity constrained programming model for the optimal design of Hazmat transportation network is constructed and the improved multi-objective genetic algorithm is used to solve the model. The case study shows the opportunity constrained programming model can better describe the optimal design of Hazmat transportation network than the traditional method under the uncertain environment. The repeating computer simulation tests show the proposed improved multi-objective genetic algorithm is feasible.
Keywords: optimisation; transportation network; multi-objective genetic algorithm; Pareto solution; hazmat transportation.
A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimization algorithm
by Poopak Azad, Nima Jafari Navimipour, Mehdi Hosseinzadeh
Abstract: Cloud computing is the latest emerging trend of distributed computing, in which distributed resources are delivered based on user's demand. In the cloud environment, computing resources need to be scheduled so that providers make the most use of the resources and users will find the applications they need at the lowest cost. So, scheduling is one of the most important issues in the cloud. Among the cloud challenges, balancing has been neglected over the rest, since balancing compliance can affect other parameters as well. The complexity of the scheduling, by considering its corresponding parameters, transforms it into an NP-hard problem. In this paper, an Inverted Ant Colony Optimization (IACO) algorithm has been used to solve the task scheduling problem in the cloud environment with the goal of reducing runtime and increasing load balancing. In the proposed method, pheromone repellent is used instead of pheromone gravity, so the effect of pheromone prevent the wrong choice. In order to observe load balance and the effect of pheromone repulsion, fuzzy logic and weight definition have been used. Finally, the proposed method has compared with First Come First Serve (FCFS), Round-Robin (RR) and Ant Colony Optimization (ACO) algorithms. The simulation results have shown that by increasing the number of tasks, the proposed algorithm while reducing the total time and cost of execution, could also increase load balance.
Keywords: Cloud computing; scheduling; inverted ant colony algorithm; makespan; load balancing; cloudsim.
Analysis of PID controller for the Load Frequency Control of Static Synchronous Series Compensator & Capacitive Energy Storage Source based Multi-area Multi-source Interconnected Power System with HVDC link
by Rajendra Khadanga, Amit Kumar
Abstract: In this research work, a maiden approach is made by using the SSSC and CES devices as a frequency damping controller in the most realistic scenario of Automatic Generation Control (AGC) of a multi-area multi-source (Thermal-Hydro-Gas) interconnected power system along with the consideration of HVDC and AC parallel tie-lines. At first, the performance of the HVDC with AC Parallel tie line is investigated for the considered power system. A PID controller is utilized to control each of the generating units in all the areas and shows its superior performance compared to other conventional controllers. A Hybrid Particle Swarm Optimization (PSO) and Gravitational search Algorithm (GSA) is proposed for tuning the gains of the PID controller and is compared with some conventional controllers. Later the effect of SSSC and CES when added as a damping controller to the power system is analyzed. It is observed that, with the addition of the SSSC and CES as a damping controller along with the optimized PID controller, the dynamic performance of the proposed interconnected power system is improved.
Keywords: Automatic Generation Control (AGC);rnLoad Frequency Control (LFC);rnMulti area multi source power system;rnFlexible A.C Transmission System (FACTS);rnCapacitive Energy Source (CES);rnHybrid PSO-GSA algorithm.
An adaptive Reinforcement Learning based Bat Algorithm for structural design problems
by Xian-Bing Meng, Han-Xiong Li, Xiao-Zhi Gao
Abstract: A Reinforcement Learning based bat algorithm is proposed for solving structural design problems. By incorporating Reinforcement Learning, the algorithms performance feedback is formulated to adaptively select between algorithms different operators. To improve the solution diversity, a new metric of individual difference is designed. The individual difference based strategies are proposed to adaptively tune the algorithms parameters. The variations of the pulse rates and loudness are newly designed to formulate their effects on the local search and foraging efficiency. Simulations and comparisons based on ten structural design problems with continuous/discrete variables demonstrate the superiority of the proposed algorithm.
Keywords: Bat Algorithm; Reinforcement Learning; Individual difference; Adaptive tuning.
IP Assignment for Efficient NoC-based System Design using Multi-objective Particle Swarm Optimization
by Maamar Bougherara, Rahmoun Rym, Sadok Amel, Nedjah Nadia, Mourelle Luiza De Macedo, Bennouar Djamel
Abstract: Network-on-chip (NoC) is considered the next generation of communication in embeddedrnsystem. In this case, an application is implemented by a set of collaborative intellectual proprietyrnblocks (IPs). The selection of the most suited block from a library of IPs is an NP-complete problem.rnIn this paper, we use Multi-Objective Particle Swarm Optimization (MOPSO) to yield the bestrnselection of IP to implement efficiently a given application on a NoC infrastructure. In this purpose,rnMOPSO is exploited to obtain an assignment that minimizes the requirements for power, hardwarernarea and the total execution of the application. We show that the achieved solutions are better thatrnthose obtained by other multi-objective optimization algorithms.
Keywords: Network-on-Chip; IP assignment; multi-objective design; particle swarm optimization.
A Review of Multiobjective Optimization and Decision Making Using Evolutionary Algorithms
by Muneendra Ojha, Krishna Pratap Singh, Pavan Chakraborty, Shekhar Verma
Abstract: Research in the field of Multiobjective Optimization Problem (MOP) has garnered ample interest in the last two decades. Majority of methods developed for solving the problem belong to the class of Evolutionary Algorithms (EA) which are population-based evolution search strategies involving exploration and exploitation in general. Multi-Criteria Decision Making (MCDM) is another aspect of MOP which involves finding methods to help a Decision Maker (DM) in making most optimal decisions in a conflicting scenario. In this paper, we present a brief review of the methods and techniques developed in the last 15 years which try to solve the MOP and MCDM problems. The strengths and weaknesses of methods have been discussed to present a holistic view. This paper covers challenges associated with MOEAs, different solution approaches such as Pareto based methods and non-Pareto methods, indicator-based methods, aggregation methods, decomposition based methods, methods using reference sets, MOEAs involving DM, a priori, interactive and a posteriori preference incorporation methods. It also discusses most of the quality metrics and performance indicators proposed in the literature along with benchmark problems. In addition, some future research issues and directions are also presented.
Keywords: Multiobjective Optimization Review;Genetic Algorithm;Evolutionary Algorithms;Multi-Criteria Decision Making.
Reconstruction of Gene Regulatory Network using S-system with Genetic Algorithm and Flower Pollination Algorithm (GA-FPA) Hybrid
by Sudip Mandal, Goutam Saha, Rajat Kumar Pal
Abstract: Accurate reconstruction of gene regulatory networks from time-series gene expression data is a significant challenge for computer scientists. In this paper, we have proposed a Genetic Algorithm and Flower Pollination Algorithm hybrid for the reverse engineering of gene regulatory network based on decoupled S-systems. Here, Genetic Algorithm has been used to select the best combination of genes, which act as regulators in the network. Flower Pollination Algorithm has been used to calculate the best possible S-system parameters for which the training error is minimum for those regulators. The proposed method has been tested on small-scale and medium-scale, synthetic benchmark networks and in-slico benchmark networks extracted from the Gene Net Weaver database, as well as the real-world experimental datasets of the yeast IMRA and DNA SOS Repair network of Escherichia coli. The experiments reveal that the proposed hybrid methodology is capable of inferring gene regulatory networks more accurately with lesser training data and in lesser computational time compared to other existing methods.
Keywords: gene regulatory networks; gene expression data; S-system; genetic algorithm; flower pollination algorithm; GeneNetWeaver; microarray; metaheuristics; reverse engineering; optimisation; cardinality; genetic regulation.
Vehicular Cloud Networking: Evolutionary Game with Reinforcement Learning based Access Approach
by Abderrezak Rachedi
Abstract: Vehicular Ad hoc Networks (VANET) have recently known a growing interest due to their benefits for both drivers and passengers. In fact, safety and non-safety applications are provided to them which make their travels safer and comfortable.
The emergence of many applications with different requirements needs better exploitation of the vehicular resources. In this context, vehicular cloud computing (VCC) is a new paradigm that aims to maximize the use of the vehicular capacities (storage, communication, and computation) opportunistically. It is based on the integration of VANET and cloud computing (CC): vehicles can share their resources or access the remote cloud to provide services. Both the access to the conventional cloud and the establishment of a temporary cloud present advantages and drawbacks: the important capacities of the CC present one of the advantages of access to the cloud while the cost of the cellular links is one of the drawbacks of access to the CC. In the case of a vehicular cloud (VC) composed
of a set of vehicles, low cost and intermittent connections can be considered as the main benefit and concern, respectively. In this paper, we study the vehicular cloud access problem. We model it as an evolutionary game where the vehicles choose to cooperate or to access the conventional cloud through the LTE link. We focus on the centralized case, and we study the equilibrium of both homogeneous and heterogeneous players analytically. We propose an Evolutionary Game-based Vehicular Cloud Access algorithm (EG-VCA). In addition, we propose a distributed Q-learning based Vehicular Cloud Access algorithm (QL-VCA) that allows each vehicle to select the way of access independently to avoid the use of a centralized controller. The simulation results show that QL-VCA and EG-VCA algorithms present almost the same performances. In addition, they offer better results compared to the cases of using and accessing only the CC or the VC. Numerical results are also established. They outline the convergence of the two algorithms to the same state of equilibrium.
Keywords: VANET; vehicular cloud networks; evolutionary game; Q-learning.
A Review of Techniques for On-line Control of Parameters in Swarm Intelligence and Evolutionary Computation Algorithms
by Rafael Parpinelli, Guilherme Plichoski, Renan Samuel Da Silva, Pedro H. Narloch
Abstract: The two major groups representing biologically inspired algorithms are Swarm Intelligence (SI) and Evolutionary Computation (EC). Such algorithms are recognized to be efficient approaches for solving complex problems. Both SI and EC share common features such as the use of stochastic components during the optimization process and various parameters for configuration. The setup of parameters of an algorithm has an important role in defining its behavior, guiding the search and biasing the quality of the solutions found. However, adjusting the parameters is not a simple task, becoming an optimization problem within the problem being optimized. In addition, an appropriate setting for the parameters may change during the optimization process making this task even harder. This article focuses on reviewing the on-line parameter tuning strategies applied in EC and SI. Also, this review analyzes and points out the key techniques and algorithms used and suggests some directions for future research.
Keywords: Parameter Control; Bio-inspired Algorithms; Meta-heuristics; Natural
Computing; Parameterless Algorithms.
A discrete particle swarm optimization algorithm to operate distributed energy generation networks efficiently
by Pablo Cortes, Jesús Muñuzuri, Luis Onieva, José Guadix
Abstract: This paper deals with the optimization of the operating costs in a distributed electric and heating energy generation network. The network considers different options to supply the electric and heating demand of a large consumer building: the electricity can be directly bought to the grid, can be taken from renewable energy sources or can be produced from gas using a combined heat and power system. In the same line, the heating can be taken from a thermal solar renewable system, from the boiler or from the combined heat and power system. In addition, the large consumer has batteries to store electricity excesses and thermal storage systems to store the heating excess. The multicommodity flow mathematical formulation of the problem couples both electric and thermal models by considering cogeneration systems. The model is solved by a Particle Swarm Optimization (PSO) algorithm that is compared to the optimal solutions provided by Gurobi optimization commercial software and a Montecarlo algorithm. The PSO algorithm proved a very efficient performance in the available short time to provide the energy commands to the systems outperforming the alternative approaches.
Keywords: particle swarm optimization; distributed energy source network; energy efficiency; multicommodity flows; cogeneration; CHP.
An Overview on Structural Health Monitoring: From the Current State-of-the-Art to New Bio-inspired Sensing Paradigms
by Maria-Giovanna Masciotta, Alberto Barontini, Luis F. Ramos, Paulo Amado-Mendes, Paulo B. Lourenco
Abstract: In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at natures creations giving rise to a new field called biomimetics, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.
Keywords: Structural health monitoring (SHM); nature-inspired sensing paradigms; bio-inspired algorithms; bio-inspired SHM sensors; biomimicry.
Multidirectional harmony search algorithm for solving integer programming and minimax problems
by Mohamed Tawhid
Abstract: Integer programming and minimax problems are important tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called Multidirectional Harmony Search Algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for number of iteration then the best obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on 7 integer programming problems and 10 minimax problems and compared against 5 algorithms for solving integer programming problems and 4 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.
Keywords: Evolutionary computation; global optimization; harmony search algorithm; direct search algorithm; multidirectional search; integer programming problems; minimax problems.
Special Issue on: New Trends in Many-Objective Optimisation
Lexi-search algorithm for one to many multidimensional bi-criteria unbalanced assignment problem
by Thenepalle Jayanth Kumar, Singamsetty Purusotham
Abstract: The classical assignment problem involves one-to-one assignment between the persons and jobs. However, most of the real world scenarios, it is hard to make a balance between persons and jobs, therefore the interest on the studies of unbalanced assignment problems continuously increased. This paper deals a one-to-many multidimensional unbalanced assignment problem with two conflicting objectives, in which the first objective minimizes the total processing time and the second objective maximizes the overall productivity/profit on performing n jobs by m (m Keywords: multidimensional bi-criteria unbalanced assignment problem; Lexi-search algorithm; pattern recognition technique.