International Journal of Bio-Inspired Computation
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International Journal of Bio-Inspired Computation (52 papers in press)
Abstract: For estimating distributions of credit-rating migrations, parallel as well as\r\nsequential heuristic methods are considered. Given a model of dependence\r\namong the migrations, the corresponding likelihood function is used for ranking\r\nbinary strings. Since not all of the macroeconomic scenarios represented by the strings make conceptual sense, heuristics for identifying an initial set of suitable macroeconomic scenarios, their mutations and evolutionary selection of the most probable of them are suggested.\r\n\r\nUsing a S&P\'s dataset, a test example\r\nwith more than 2^42 unknowns is analyzed.
Keywords: heuristics; GA encoding; mutation; parallel; sequential; maximum likelihood; selection.
Adaptive Neighborhood Size Adjustment in MOEA/D-DRA
by Meng Xu
Abstract: The multiobjective optimization algorithms based on decomposition(MOEA/D) is a well-known multiobjective optimization algorithms(MOEAs). MOEA/D was proposed by Zhang and Li in 2007s. MOEA/D decomposes a multiobjective problem into a set of scalar single objective subproblems using the aggregation function and the evolutionary operator. The variant of the dynamic resource allocation strategy in MOEA/D(MOEA/D-DRA) has the outstanding performance on CEC2009, the MOEA/D-DRA using the strategy of resource allocation. It cares about the convergence and ignores the diversity. MOEA/D-DRA is very sensitive to the neighbourhood size. In this paper, we present a new enhanced MOEA/D-DRA strategy based on the adaptive neighbourhood size adjustment(MOEA/D-DRA) to increase the diversity. It focuses on the solutions density around of subproblems. The experiment results demonstrate that MOEA/D-ANA strategy performs the best compared with other five classical MOEAs on the CEC2009 test instances.
Keywords: MOEA/D; diversity; Neighborhood; CEC2009 test instances;.
Recognition of driver emergency braking behavior based on support vector machine optimized by memetic algorithm
by Shenpei Zhou, Bingchen Qiao, Haoran Li, Bin Ran
Abstract: Surface electromyography (sEMG) is one of the main information sources of human motion detection and has been widely used. The lower limb sEMG signal is introduced into the recognition model of driver emergency braking behavior, and the features from time domain, frequency domain and model parameters are extracted to construct a feature vector. In addition, to improve recognition accuracy, the data from conventional braking and accelerated shifting behaviors similar to the characteristics of emergency braking are synchronously collected. These three driving behaviors are identified by using support vector machine (SVM), and a memetic algorithm (MA) based on particle swarm optimization and hill climbing algorithm is proposed to optimize the parameters of SVM. The results show that the model based on SVM optimized by MA has better classification performance than that without optimization. The final recognition rate of emergency braking behavior of same individual is up to 92.3%, and that of different individuals can reach 85.6%. Moreover, the system can detect emergency braking 220 ms earlier than operating brake pedal. At 100 km/h driving speed, this amounts to reducing the braking distance by 6.1 m.
Keywords: sEMG; emergency braking; SVM; parameter optimization; memetic algorithm; particle swarm optimization; hill climbing algorithm.
Swarm and Evolutionary Algorithms for Energy Disaggregation: Challenges and Prospects
by Samira Ghorbanpour, Trinadh Pamulapati, Rammohan Mallipeddi
Abstract: Energy disaggregation is defined as the process of estimating the individual electrical appliance energy consumption of a set of appliances in a house from the aggregated measurements taken at a single point or limited points. The energy disaggregation problem can be modelled both as pattern recognition problem and as an optimization problem. Among the two, the pattern recognition problem has been considerably explored while the optimization problem has not been explored to the potential. In literature, researchers have attempted to solve the problem using various optimization algorithms including swarm and evolutionary algorithms. However, the focus on optimization-based methodologies, in general, swarm and evolutionary algorithm based methodologies in particular is minimal. By considering the different problem formulations in the literature, we propose a framework to solve the energy disaggregation problem with swarm and evolutionary algorithms. With the help of simulation results using the existing problem formulations, we discuss the challenges posed by the energy disaggregation to swarm and evolutionary algorithm based methodologies and analyse the prospects of these algorithms for the problem of energy disaggregation with some future directions.
Keywords: Energy Disaggregation; Swarm and Evolutionary Algorithms.
An Improved Brain Storm Optimization Algorithm for Energy-efficient Train Operation Problem
by Boyang Qu, Qian Zhou, Yongsheng Zhu, Jing Liang, Caitong Yue, Yuechao Jiao, Li Yan
Abstract: This paper presents a new method to determine the optimal driving strategies of the train using an improved brain storm optimization (IBSO) algorithm. In the proposed method, the idea of successful-parent-selecting frame is applied to improve the original brain storm optimization (BSO) algorithm avoiding premature convergence in evolutionary process while dealing with complex problems. The objective of the algorithm is to minimum energy consumption of the train by nding the switching points. Furthermore, the speed limits, gradients, maximum acceleration and deceleration as well as the maximum traction and braking force varying with speed are taken into consideration to meet practical constraints. Finally the comparison simulations among four algorithms show that the energy-efficient train operation strategy obtained by IBSO algorithm are more superior under same conditions.
Keywords: Evolutionary algorithms; Brain Storm Optimization; Energy-efficient train operation.
Flight Control System Design Using Adaptive Pigeon-Inspired Optimization
by Mostafa Saad, Duan Haibin
Abstract: Pigeon-Inspired Optimization (PIO) algorithm is a swarm intelligence algorithm inspired by homing behavior of pigeons. Adaptive Pigeon-Inspired Optimization (APIO) algorithm is introduced to provide better search efficiency and faster convergence speed than Pigeon-Inspired Optimization (PIO) algorithm. Through (APIO), the optimization algorithm will better deal with the flying vehicles and the optimization process can reach an optimum solution during the control cycle time of the control system rather than Pigeon-Inspired Optimization (PIO) algorithm. In this paper the flight control system for a tactical missile is presented using classical controller (PID), and the controller gains will be calculated using both (PIO) and (APIO) during vehicle flight. Platform simulation is presented to evaluate the performance of both algorithms.
Keywords: Flying vehicle; Tactical missile; Autopilot design; Pigeon-inspired optimization (PIO); Adaptive Pigeon-inspired optimization (APIO); Classical controller (PID); Simulation.
A Two-Lane Mixed Traffic Flow Model with Drivers' Intention to Change Lane Based on Cellular Automata
by Changbing Jiang, Ruolan Li, Tinggui Chen, Chonghuan Xu, Liang Li, Shufang Li
Abstract: Aiming at the deficiencies of classic NaSch model and two-lane lane changing model (STCA), based on the improvement of the rules and lane changing rules, the viaduct is taken as the simulation objective. Under the open boundary condition, a two-lane mixed traffic flow model considering the intention to enter the lane is established. Considering the need of driver to enter lane research, this model puts forward the vehicle enter rules, which takes into account the relevant factors that affect drivers intention to enter the road and quantify the influencing factors through the method of average input rate in queuing theory. Numerical simulation experiment shows that the average input rate ? is determined by input rate ? and output rate ?, and is affected by the sensitivity parameter ?, and ? is not greater than ?. The passing state of a road vehicle is determined by the output rate ? and is affected by ?. At the same time, compared with the model that ignores the drivers intention to enter the road, the model proposed in this paper is closer to the actual traffic situation.
Keywords: Average arrival rate;cellular automata;drivers'intention to change lane;mixed traffic flow.
An Effective Improved Co-evolution Ant Colony Optimization Algorithm with Multi-Strategies and Its Application
by Wu Deng, Huimin Zhao
Abstract: In this paper, an effective improved co-evolution ant colony optimization(MSICEAO) algorithm is presented to solve complex optimization problem. In the MSICEAO, the multi-population co-evolution strategy is used to divide initial population into several sub-populations to interchange and share information. The weighted initial pheromone distribution strategy is used to improve the efficiency and adjust the pheromone factor and distance factor. The elitist retention strategy is used to improve the solution quality. The adaptive dynamic update strategy for pheromone evaporation rate is used to balance the convergence speed and solution quality. The aggregation pheromone diffusion mechanism is used to enhance the cooperative effect and highlight the cooperative idea of swarm intelligence. In order to verify the effectiveness of the MSICEAO, the experiments have been carried out on 8 TSPs and one actual gate allocation problem. The MSICEAO is compared with five state-of-the-art algorithms of TS, GA, PSO, ACO and PSACO. The experiment results demonstrate that the MSICEAO is significantly better than the compared methods.
Keywords: Ant colony optimization; Multi-population co-evolution; Elitist retention; Pheromone distribution and diffusion; Adaptive dynamic update; Gate allocation.
Multi-objective optimization framework of genetic programming for investigation of Bullwhip effect and Net Stock Amplification for three-stage supply chain systems
by Akhil Garg, Surinder Singh, Liang Gao, Xu Meijuan
Abstract: To address the concerns on large variance in demand orders and stocks at inventory level (referred as bullwhip effect and net stock amplification (NSA)), the methods based on reducing uncertainty, variability, lead times and forming strategic partnerships can be applied. However, these methods could not eliminate the effect entirely but can only reduce it. The four input factors mainly affecting the bullwhip effect and NSA includes the demand signal processing, batch order, lead time and rationing and shortage gaming. The research problem of studying the impact of these four input factors on the bullwhip effect and NSA, and determining the key trade-off between them can be solved by the notion of mathematical modeling. Therefore, this work will propose a multi-objective optimization framework of genetic programming (GP) in the modeling of bullwhip effect and NSA for centralized and decentralized supply chain systems. The individual and interactive effect of these four input factors has been investigated on bullwhip effect and NSA by adapting the parametric and sensitivity approach on the formulated models. The appropriate settings of dominant input factors (batch ordering and demand signal processing for a decentralized chain, demand signal processing and rationing shortage gaming for a centralized chain) are suggested to optimize the bullwhip effect and NSA of three-stage supply chain simultaneously. The implications and advantages of proposed optimization framework will be useful for business practitioners to monitor and supervise the sudden demand amplification that generally faced by them in the supply chains.
Keywords: bullwhip effect; net stock amplification; genetic programming; modeling; optimization.
Forward Feature Extraction from imbalanced microarray datasets using Wrapper based Incremental Genetic Algorithm
by Devi Priya Rangasamy, Sivaraj Rajappan
Abstract: Learning from imbalanced datasets is a critical challenge confronting researchers. Unequal distribution of classes in the imbalanced datasets lead to biased classification es-pecially in microarray gene expression analysis. Another important concern for researchers is that the microarray datasets usually contain huge number of features with few samples. Since all features in the dataset will not contribute to the analysis, only prominent and sig-nificant features need to be identified. The paper addresses both these issues by proposing Wrapper based Incremental Genetic Algorithm (IGA) which incrementally evaluates and adds attributes into the Genetic Algorithm process rather than evaluation of all attributes thereby reducing the computational complexity and number of features used and improving the measures like classification accuracy, GMean, F1 measure, precision and recall. The experiments are conducted on 8 microarray gene expression datasets and the results show that performance of IGA is encouraging and superior to existing methods that are compared.
Keywords: Incremental Genetic Algorithm; Imbalanced dataset; Bootstrap sampling; Forward Feature Extraction; Adaptive Mutation.
A path planning method for UAV based on multi-objective pigeon-inspired optimization and differential evolution
by Bingda Tong
Abstract: Inspired by the behavior of pigeon flocks, an improved method of path planning and autonomous formation for unmanned aerial vehicle based on the pigeon-inspired optimization and differential evolution is proposed in this paper. Firstly, the mathematical model for UAV path planning is devised as a multi-objective optimization with three indices, i.e., the length of a path, the sinuosity of a path, and the risk of a path. Then the method integrated by pigeon-inspired optimization and mutation strategies of differential evolution is developed to optimize feasible paths. Besides, Pareto dominance is applied to select global best position of a pigeon. Finally, a series of simulation results compared with standard particle swarm optimization algorithm and standard differential evolution algorithm show the effectiveness of our method.
Keywords: path planning;unmanned aerial vehicle;pigeon-inspired optimization;differential evolution.
Enhanced Pigeon Inspired Optimization Approach for Agile Earth Observation Satellite Scheduling
by Shang Xiang, Lining Xing, Ling Wang, Yongquan Zhou, Guansheng Peng
Abstract: The agile earth observe satellite scheduling problem has been one of the most difficult problem in the field of operations research. For agile satellite, a different observation time means a different observing angle, thus defining a different transition time from the adjacent tasks. Therefore, the agile earth observe satellite scheduling problem has a features of time-dependent which make the solving more difficult. This article formulated a new model of agile earth observe satellite scheduling problem and designed a new solving frame. Enhanced pigeon-inspired optimization was proposed for solving it, in which guide operator and accelerate operator are introduced to enhance the search capability of the algorithm. After test on 15 experimental scenarios, the proposed algorithm own a better performance than the state-of-the-art algorithm.
Keywords: Agile satellite scheduling; pigeon-inspired optimization; guide operator; accelerate operator.
An Improved Discrete Pigeon-Inspired Optimization Algorithm for Flexible Job Shop Scheduling Problem
by Xiuli Wu, Xianli Shen, Ning Zhao
Abstract: The pigeon-inspired optimization (PIO) algorithm, which is a new promising optimization algorithm, has successfully solved many continuous optimization problems. In the literature, however, little research has been conducted on its application to the combinational optimization problems. This paper therefore tries to fill in this gap and applies the PIO algorithm to solve the flexible job shop scheduling problem (FJSP), which is a typical combinational optimization problem. It proposes an improved discrete PIO (IDPIO) algorithm to minimize the makespan of FJSP and develops methods to optimize the time to carry out the map and compass operator or the landmark operator with the PIO. The discrete map, compass operator, and the discrete landmark operator are developed respectively. The experiment results show that the IDPIO algorithm can solve the FJSP effectively and efficiently.
Keywords: Discrete pigeon-inspired optimization algorithm; Flexible job shop scheduling problem; Discretization; Map and compass operator; Landmark operator.
Computer Assisted Medical Decision Making System using Genetic Algorithm and Extreme Learning Machine for Diagnosing Allergic Rhinitis
by Elgin Chrsito V.R, H. Khanna Nehemiah, Kindie Nahato, Brighty J, Kannan Arputharaj
Abstract: Allergic Rhinitis (AR) is an antigen-mediated inflammation of the nasal mucosa that might extend into the paranasal sinuses. Rhinorrhea, nasal obstruction or blockage, nasal itching, sneezing, and postnasal drip that reverse spontaneously or after treatment are symptoms of AR. Allergic conjunctivitis frequently accompanies AR. For diagnosis of AR, intradermal skin tests remain the gold standard. This paper presents a clinical decision-making system that assists the clinicians to diagnose whether a patient suffers from AR. Feature selection is done using a wrapper approach that employs genetic algorithm (GA) and Extreme Learning Machine (ELM). The selected features are trained and tested using an ELM classifier. For experimenting, the outcome of the symptoms observed in 872 patients for diagnosing the presence or absence of AR has been used. The experimental result shows that the system has achieved an accuracy of 97.7%.
Keywords: Genetic Algorithm; Extreme Learning Machine; Computer assisted decisionrnmaking; Allergy and Immunology; Machine learning.
Magnetotactic bacteria optimization algorithm with self-regulation interaction energy
by Jiao Zhao, Hongwei Mo
Abstract: Magnetotactic bacteria optimization algorithm (MBOA) is a new optimization algorithm inspired by the biology characteristics of magnetotactic bacteria in the nature. The original MBOA is vulnerable to premature convergence and has poor exploration capability. In this paper, a modified magnetotactic bacteria optimization algorithm (MMBOA) is proposed to improve the performance of algorithm. It uses a kind of self-regulation interaction energy to enhance the diversity of the swarm for encouraging broader exploration. And as the number of iterations increases, the self-regulation interaction energy can keep MMBOA convergence. Convergence analysis of MMBOA is also implemented. The proposed algorithm converges to the global optimum with a probability one when the number of iterations tends to infinity. Experimental results on CEC2013 and a part of CEC2011 benchmark functions show that the proposed algorithm is efficient and robust, especially in optimizing multimodal functions.
Keywords: Magnetotactic bacteria optimization algorithm; self-regulation interaction energy; exploration; global convergence.
Bee-inspired Task Allocation Algorithm for Multi-UAV Search and Rescue Missions
by Heba Kurdi, Shiroq Al-Megren, Ebtesam Aloboud, Abeer Alnuaim, Hessah Alomair, Reem Alothman, Alhanouf Ben Muhayya, Noura Alharbi, Manal Alenzi, Kamal Youcef-Toumi
Abstract: Task allocation plays a pivotal role in the optimization of multi-unmanned aerial vehicle (multi-UAV) search and rescue (SAR) missions in which the search time is critical and communication infrastructure is unavailable. These two issues are addressed by the proposed BMUTA algorithm, a bee-inspired algorithm for autonomous task allocation in multi-UAV SAR missions. In BMUTA, UAVs dynamically change their roles to adapt to changing SAR mission parameters and situations by mimicking the behavior of honeybees foraging for nectar. Four task allocation heuristics (auction-based, max-sum, ant colony optimization, and opportunistic task allocation) were thoroughly tested in simulated SAR mission scenarios to comparatively assess their performances relative to that of BMUTA. The experimental results demonstrate the ability of BMUTA to achieve a superior number of rescued victims with much shorter rescue times and runtime intervals. The proposed approach demonstrates a high level of flexibility based on its situational awareness, high autonomy, and economic communication scheme.
Keywords: Task Allocation; Bio-Inspired Algorithms; Unmanned Aerial Vehicles; Distributed Systems; Search and Rescue; Optimization Problems.
Inspiration-wise Swarm Intelligence Meta-heuristics for Continuous Optimization: A Survey--- Part II
by Nedjah Nadia, Luiza De Macedo Mourelle, Reinaldo Morais
Abstract: Optimization is a a tool required in many sciences such as engineering, administration, logistics, transportation, economics and biology, among others. Global optimization techniques aim to find the best solution in a set of feasible ones. Meta-heuristics are general algorithmic frameworks adaptable to various optimization problems. In generally, these are applied to highly complex problems. Swarm Intelligence represents intelligent models inspired by existing social systems, based on interaction and organization between simple individuals to perform simple tasks. In the literature, there are many interesting swarm based meta-heuristics. It always is useful to have a taxonomy for the current state-of-the-art of swarm-oriented meta-heuristics. In the first part of this survey, we proposed an extensible such a taxonomy that is based on the inspiration behind the search strategy of the technique.\r\nThis work as a whole provides a survey of swarm based meta-heuristic, which are commonly employed to solve complex continuous optimizations, aiming at building a inspiration-based taxonomy for such search strategies. In this second part of the survey, we propose a review of existing swarm-oriented search strategies, categorized according to fictional metaphors\' inspiration as well as flocking and schooling inspirations, which in turn are considered to be bio-inspired. For each of the included technique, we explain the essence of the swarm search strategy.
Keywords: Swarm Intelligence; Swarm-based meta-heuristics; Bio-inspired computation.
Formation control of multiple UAVs via pigeon swarm optimization
by B.A.I. TINGTING, WANG DAOBO, Zain Anwar Ali, Suhaib Masroor
Abstract: A distributed coordinated control is proposed for the group of UAVs to map the behaviour rule for the pigeon group. Moreover, on the basis of pigeon inspired optimization (PIO), the UAV groups are clustered and synchronized through the coordinated control algorithm. To control the speed, direction of nth UAVs for the desired formation of flight are controlled by proportional integral (PI) and proportional integral differential (PID) controller. A high degree of convergence control method is presented to control the UAV groups at the same level of height during formation of flight. On the basis of pigeon flight obstacle avoidance, a method of UAV obstacle avoidance is designed, and a method of obstacle obstructing is also designed along with the improved artificial physical method. The proposed methodology provides obstacle avoidance for a group of UAV and overcome traditional physical methods, and can easily make the desired formation in which sudden obstacles lie in the way of formation through the arrow channel.
Keywords: Pigeon Swarm Optimization; Unmanned Aerial Vehicle; Pigeon Behavior and Coordinated Control.
An improved NSGA-II with dimension perturbation and density estimation for multi-objective DV-Hop localization algorithm
by Yang Cao, Li Zhou, Fei Xue
Abstract: NSGA-II is a well-known multi-objective optimization algorithm, which has shown excellent performance on many multi-objective optimization problems. However, the classical NSGA-II suffers from uneven distribution of convergence, poor global search ability. To address these issues, this paper proposes an improved NSGA-II (INSGA-II) by employing two strategies: a crossover operation based on dimension perturbation and a novel updating operation based on average individual density estimation. Then the INSGA-II is applied to optimize the multi-objective DV-Hop localization algorithm. To verify the effectiveness of proposed INSGA-II, we compare it with four other multi-objective evolutionary algorithms on six benchmark functions. Simulation results show that our approach outperforms other compared algorithms. What more, the performance of DV-Hop algorithm based on INSGA-II is tested by the simulation experiments. The simulation results show that DV-Hop localization with INSGA-II achieves better localization accuracy than that with CS, WOCS, MODE and NSGA-II.
Keywords: NSGA-II; dimension perturbation; density estimation; multi-objective optimization; DV-Hop localization algorithm.
Improved Gravitational Search Algorithm Based on Chaotic Local Search
by Zhaolu Guo, Wensheng Zhang, Shenwen Wang
Abstract: The traditional gravitational search algorithm (GSA) maintains good diversity of solutions but often demonstrates weak local search ability. To promote the local search ability of GSA, a new GSA based on chaotic local search (CLSGSA) is introduced in this paper. In its search operations, CLSGSA first executes the conventional search operations of the basic GSA to maintain the diversity of solutions. After that, CLSGSA executes a chaotic local search with the search experience from the current best solution to increase the local search capability. In the experiments, we utilize a suite of benchmark functions to verify the performance of CLSGSA. Moreover, we compare the proposed CLSGSA with several GSA variants. The comparisons validate the effectiveness of CLSGSA.
Keywords: Evolutionary algorithm; optimization algorithm; gravitational search; local search; chaotic map.
A modified single and multi-objective bacteria foraging optimization for the solution of quadratic assignment problem
by Saeid Parvandeh, Mohammadreza Boroumand, Fahimeh Boroumand, Pariya Soltani
Abstract: Non-polynomial hard (NP-hard) problems are challenging because no polynomial time algorithm has yet been discovered to solve them in polynomial time. The Bacteria Foraging Optimization (BFO) algorithm is one of the metaheuristics algorithms that is mostly used for NP-hard problems. BFO is inspired by the behavior of the bacteria foraging such as Escherichia coli (E-coli). The aim of BFO is to eliminate those bacteria that have weak foraging properties and maintain those bacteria that have breakthrough foraging properties toward the optimum. Despite the strength of this algorithm, most of the problems reaching optimal solutions are time-demanding or impossible. In this paper, we modified single objective BFO by adding a mutation operator and multi-objective BFO (MOBFO) by adding mutation and crossover from genetic algorithm operators to update the solutions in each generation, and local tabu search algorithm to reach the local optimum solution. Additionally, we used a fast nondominated sort algorithm in MOBFO to find the best nondominated solutions in each generation. We evaluated the performance of the proposed algorithms through a number of single and multi-objective Quadratic Assignment Problem (QAP) instances. The experimental results show that our approaches outperform some previous optimization algorithms in both convergent and divergent solutions.
Keywords: multi-objective bacteria foraging optimization; genetic algorithm; local tabu search; quadratic assignment problem.
Latent Dactyloscopy Pairing: Presentation Attain Through Feedback from EPITOME
by Pugalenthi R, Mohan Kumar, Arokia Renjith
Abstract: Latent fingerprints play a vital role for recognizing the criminals in the law enforcement agencies. It provides evidence for the court in the law. When the method called AFIS (Automatic Fingerprint Identification System) is applied to rolled and plain fingerprints, it gives a result of high accuracy. Latent accuracy will be less due to the presence of complex background noise, poor ridge quality. In latent the pairing of latent images is a demanding problem. We suggest including top-down data or feedback from an epitome to filter the features taken from a latent for developing latent pairing exactness. The filtered latent features (e.g. ridge orientation and frequency), after feedback, are utilized to re-match the latent to the top X candidate epitome put back by the starting line pairer and retreat the candidate record. The benefactions of this analysis involve: (i) framing ordered steps to utilize data in epitome for latent feature filtration, (ii) improving a feedback pattern which can be delighted around any latent pairer for developing its pairing production, and (iii) deciding when feedback is literally needed to develop latent pairing exactness. Research outcome reveal that combing the proposed feedback pattern with a state-of-the-art latent pairer develops its finding exactness by 0.5-3.5% for NIST SD27 and WVU latent databases against a background database of 100X epitome.
Keywords: Candidate record; Feature filtration; latent fingerprint pairing; epitome feedback.
Expediting Population Diversification in Evolutionary Computation with Quantum Algorithm
by Jun Suk Kim, Chang Wook Ahn
Abstract: Attempts to introduce quantum application to various fields in computer science are growing in numbers as days of commercialized, fully functional quantum computers come closer. Quantum computing\'s uniqueness in commencing parallel computation renders unprecedentedly efficient optimization possible. This paper introduces the adaptation of quantum processing to Crowding, one of the genetic algorithmic procedures to secure undeveloped individual chromosomes in pursuit of diversifying the target population. We argue that the nature of genetic algorithm to find the best solution in the process of optimization can be greatly enhanced by the capability of quantum computing to perform multiple computations in parallel. By introducing the relevant quantum mathematics based on Grover\'s Selection Algorithm and constructing its mechanism in a quantum simulator, we come to conclusion that our proposed approach is valid in such a way that it can precisely reduce the amount of computation query to finish the crowding process without any impairment in the middle of genetic operations.
Keywords: Quantum Computing; Quantum Evolutionary Algorithm; Population Diversification.
Sine-Cosine-Algorithm Based Fractional Order PID Controller Tuning for Multivariable Systems
by Jailsingh Bhookya, Ravi Kumar Jatoth
Abstract: The multivariable systems, used in several industrial processing systems, are controlled using multi-loop controllers. A novel fractional order proportional integral derivative (FOPID) controller design without de-coupler for the multivariable system using an optimization algorithm is proposed. The FOPID controller is employed in each loop of a two-input-two-output (TITO) system and their parameters are tuned using Sine-Cosine-Algorithm (SCA). The proposed method eliminates the interaction of loops with the optimal tuning of FOPID controller parameters. The proposed FOPID design for the Wood-Berry TITO system is compared with the recent state-of-the-art literature to verify its overall efficiency. The simulation result demonstrates the superiority of the proposed controller over other controller designs for the TITO system.
Keywords: Fractional Order PID; Multivariable; TITO System; Multi-loop Control; Sine Cosine Algorithm; Optimal Tuning.
A modified machine learning classification for dental age assessment with effectual ACM-JO based segmentation
by Hemalatha Balan
Abstract: Estimation of Dental age plays a vital role in anthropology, forensics, and bio archaeology. Specific age estimation is mandatory, for living and dead individuals, especially in young adolescents and children. Diverse detection of dental age schemes is calculated, still they have certain limitations such as reliability and prediction accuracy was not superior. To resolve this, Modified Extreme Learning Machine with Sparse Representation Classification (MELM-SRC) is anticipated with dental image in this work. Initially, input image is preprocessed for reducing noise and smoothing in image using Anisotropic Diffusion Filter (ADF). Subsequently, teeth image are segmented using Active Contour Model (ACM) with Jaya Optimization (JO) and then morphological post processing has been applied on segmented result to progress classification accuracy. Next, certain features are extracted such as area, perimeter, solidity, Diameter, major and minor axis length, and filled area to enhance prediction accuracy. Lastly, age has been classified with MELM-SRC. In this MELM, effectual features are classified using SRC to increase age classification accuracy. Simulation outcomes show anticipated MELM-SRC acquires superior performance than Demirjian method for dental age assessment and also other exiting classification schemes such as Radial Basis Function Network (RBFN), and Adaptive Neuro Fuzzy Inference System (ANFIS) schemes.
Keywords: Dental age (DA); Anisotropic Diffusion Filter (ADF); Active Contour Model (ACM); Jaya Optimization (JO) algorithm; Modified Extreme Learning Machine (MELM); Sparse Representation Classification (SRC); Radial Basis Function Network (RBFN); Adaptive Neuro Fuzzy Inference System (ANFIS).
Research on the ensemble feature selection algorithm based on multimodal optimization techniques
by Yan-li Wang, Bo-yang Qu, Jing Liang, Yi Hu, Yun-peng Wei
Abstract: Feature selection is essentially a high-dimensional combinatorial optimization problem. To find representative feature subsets, the selection method needs powerful exploration ability. In addition, if alternative feature subsets could be provided, the final prediction accuracy can be improved by ensembling these subsets. Multimodal optimization (MO) methods with high exploration power and can find multiple suitable solutions in one single run. Therefore, this paper presents an ensemble feature selection algorithm based on multimodal optimization techniques. Differential evolution based on fitness Euclidean-distance ratio (FERDE) algorithm is utilized to search for multiple diverse feature subsets in the huge feature space. A set of diverse base classifiers are built based on these subsets and ensemble to improve the final classification performance. Compared with several existing classical algorithms and ensemble feature selection methods, the proposed method can achieve higher predictive accuracy.
Keywords: feature selection; ensemble learning; multimodal optimization; classification.
A Novel Oversampling Technique Based on the Manifold Distance for Class Imbalance Learning
by Yinan Guo, Botao Jiao, Lingkai Yang, Jian Cheng, Shengxiang Yang, Fengzhen Tang
Abstract: Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space is ineffective for the imbalance datasets with class overlapping or small disjunction. Based on this, a novel oversampling technique based on manifold distance is proposed, in which a new minority sample is produced in terms of the distances among neighbors in manifold space, rather than the Euclidean distance among them. After mapping the original data to its manifold structure, the overlapped majority and minority samples will lie in areas easily being partitioned. In addition, the new samples are generated based on the neighbors locating nearby in manifold space, avoiding the adverse effect of the disjoint minority classes. Following that, an adaptive adjustment method is presented to determine the number of the newly generated minority samples according to the distribution density of the matched-pair data. The experimental results on 48 imbalanced datasets indicate that the proposed oversampling technique has the better classification accuracy.
Keywords: (class imbalance learning; oversampling; manifold learning; overlapping; small disjuncts).
A new approach to design S-Box generation algorithm based on genetic algorithm
by Unal Cavusoglu, Abdullah Hulusi Kokcam
Abstract: Substitution box (S-Box) is one of the most important structures used for byte change operation in block encryption algorithms. An S-Box structure with strong cryptological properties makes the encryption algorithm much more resistant to attacks. In this article, a powerful S-Box generation algorithm (GA) design is presented using genetic algorithm. In the genetic algorithm based S-Box generation algorithm, the nonlinearity value which is one of the most important of the S-Box evaluation criteria, has been processed. Quality of the generated S-Boxes is determined by performance tests. Obtained performance results are compared with the S-Boxes in the literature. It has been found that the presented algorithm generates S-Boxes with strong cryptological properties.
Keywords: Genetic Algorithms; Information Security; Nonlinearity; S-Box.
Cauchy-Gaussian pigeon-inspired optimization for electromagnetic inverse problem
by Mengzhen Huo, Yimin Deng, Haibin Duan
Abstract: The optimization of electromagnetic inverse problems could be attributed to a constraint nonlinear programming problem. Loneys solenoid problem is one of the electromagnetic inverse benchmarks in the magnetic field. Parameters such as the structure and medium are necessary to be designed based on the required magnetic properties. In this paper, an improved variant of pigeon-inspired optimization (PIO) algorithm based on Cauchy distribution and Gaussian distribution, named Cauchy-Gaussian pigeon-inspired optimization (CGPIO), is proposed to solve the electromagnetic inverse problems. The PIO algorithm is a bio-inspired swarm intelligence optimization algorithm, which imitates the homing process of pigeons. To improve the convergence efficiency of the basic PIO algorithm, two operators including Cauchy distribution and Gaussian distribution are utilized. Comparative results show the suitability and superiority of CGPIO algorithm for electromagnetic optimization.
Keywords: Pigeon-inspired optimization; electromagnetic inverse problem; Loney’s solenoid problem; Cauchy distribution; Gaussian distribution.
A real adjacency matrix-coded evolution algorithm for highly linkage-based routing problems.
by Hang Wei, Han Huang, Zhi-Feng Hao, Witold Pedrycz, Qin-Qun Chen, Gang Li
Abstract: In routing problems, the contribution of a variable to ?tness often depends on the states of other variables. This phenomenon is referred to as linkage. High linkage level typically makes a routing problem more challenging for an evolutionary algorithm (EA). An entire linkage measure, named entire linkage index (ELI), has been proposed in this paper for such routing problems. Aiming at solving high linkage-based routing problems, we presented a real adjacency matrix-coded evolution algorithm (RAMEA) that is capable of learning and evolving correlation matrix of decision variables. The e?ciency of RAMEA was tested on two familiar routing problems: traveling salesman problem (TSP) and generalized traveling salesman problem (GTSP). The experimental results show that the RAMEA is promising for those highly linkage-based routing problems, especially for those of large scale.
Keywords: linkage; routing problems; evolutionary optimization; real adjacency matrix-coding mechanism.
Comparison of Three Nature Inspired Molecular Docking Algorithms
by Petra Cechová, Martin Kubala
Abstract: Molecular docking uses different methods to generate and evaluate the binding between a receptor and a ligand. Three nature-inspired docking programs are compared on a test set of 65 receptor-ligand pairs. AutoDock uses a genetic algorithm inspired by the process of natural selection; PSOVinaLS uses the Particle Swarm Optimization method, based on the behaviour of animal flocks and PLANTS uses an algorithm based on ant colonies. Using the default parameters, PSOVinaLS achieved the best performance with respect to time required and docking accuracy, followed by PLANTS and, with a large gap, AutoDock. However, all the programs exhibited difficulties with redocking of ligands with more than 10 rotable bonds.
Keywords: Molecular docking; redocking study; genetic algorithm; particle swarm optimization; ant colony optimization;.
An Integrated Data Mining Approach to Predict Electrical Energy Consumption
by Alireza Fallahpour, Kaveh Barri, Kuan Yew Wong, Pengcheng Jiao, Amir H. Alavi
Abstract: This study proposes an integrated adaptive neuro fuzzy inference system (ANFIS) and gene expression programming (GEP) approach to predict long-term electrical energy consumption. The developed hybrid method uses ANFIS to find parameters with maximum effect on the electricity demand. Thereafter, the GEP algorithm is deployed to derive a robust mathematical model for the prediction of the electricity demand. Various statistical criteria are considered to verify the validity of the model. The predictions made by the ANFIS-GEP model are compared with those obtained by the simple GEP and hybrid artificial neural network (ANN)-ANFIS methods. The proposed ANFIS-GEP technique is more computationally efficient and accurate than GEP, and notably outperforms ANFIS-ANN.
Keywords: Electricity demand forecasting; Feature selection; ANFIS; GEP; Formulation.
A New Hybrid System Combining Active Learning and Particle Swarm Optimization for Medical Data Classification
by Nawel Zemmal, Nabiha Azizi, Mokhtar Sellami, Soraya Cheriguene, Amel Ziani
Abstract: With the increase of unlabeled data in medical datasets, the labelling process becomes a more costly task. Therefore, Active Learning provides a framework to reduce the amount the manual labor process by querying an expert for just the labels of particular instances, the choice of these instances to annotate is paramount. However, the traditional active learning techniques can be computationally expensive as they require to analyze, at each iteration, all unlabeled instances including those that are redundant and uninformative, thereby decreasing the system performance. To handle this issue, it is necessary to have a global optimization algorithm that allows finding the best solution in a reasonable time. This paper proposes a novel framework combining active learning and particle swarm optimization algorithm. A novel uncertainty-based strategy was designed and integrated into the PSO as an objective function. This new strategy allows finding the most informative instances by calculating an uncertainty score using instance weighting method. Experiments were performed on binary and multi-class classification problems using both balanced and unbalanced medical datasets. Experimental results show that the proposed uncertainty strategy outperforms its existing counterparts. It achieves performances comparable to supervised methods.
Keywords: Active Learning; Uncertainty Sampling Strategy; Particle Swarm Optimization; Global Optimization Problem; Instance Weighting.
Density peaks clustering based on geodetic distance and dynamic neighborhood
by Li Lv, Jiayuan Wang, Runxiu Wu, Hui Wang, Ivan Lee
Abstract: Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples, and it can achieve a good clustering effect when processing the manifold datasets. Utilizing this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighborhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbors K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other 5 clustering algorithms on 6 synthetic datasets and 10 real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets.
Keywords: Density peaks; clustering; geodetic distance; dynamic neighborhood.
DVR Based Power Quality Enhancement Using Adaptive Particle Swarm Optimization (APSO) Technique
by Ravi Srinivas Lanka, Mahesh Babu Basam, Tulasi Ram S.S.
Abstract: This paper proposes a heuristic control of the series active power filter for power quality enhancement. In the context of power quality, the series active filter is better utilized as a voltage source controller contrary to its conventional usage as variable impedance. The present-day utility system as a linear model is unsatisfactory and the steps are laid down to discuss utility systems as a nonlinear model. This paper deals with comparative analysis of particle swarm optimization (PSO) and its contemporary modified PSO algorithms modified PSO (MPSO), Adaptive PSO (APSO). The harmonic reduction in the source current and sags/swells mitigation in the load voltage is carried out with the optimal tuning of the PI controller. The Series active power filter as a harmonic suppressor with a specific reference controlled strategy is discussed in this paper. The synchronous reference frame (SRF) Theory and its modified versions are used to generate a compensating signal. The hysteresis band current controller (HBCC) is used to perform the switching operation of Voltage Source Inverter. Simulations are carried out in the MATLAB/SIMULINK environment.
Keywords: Dynamic Voltage Restorer (DVR); Synchronous Reference Frame theory (SRF); Particle Swarm Optimization (PSO); Modified PSO (MPSO); Adaptive PSO (APSO).
Pigeon-inspired optimization algorithm with hierarchical topology and receding horizon control for multi-UAV formation
by Yankai Shen, Yiming Deng
Abstract: Multi-UAV formation is a crucial research aspect of UAV cooperation control. This paper proposes a hierarchical topology pigeon-inspired optimization (HPIO) and a receding horizon control (RHC) to deal with the problem. The RHC method can convert the problem into an online optimization problem. Besides, inspired by the structure of pigeon flock, a new topology with a hierarchical structure of pigeons is designed. The velocity updating formula of the original PIO is redesigned. Then discuss the stability of the modified PIO. Finally, use the HPIO to generate the desired formation control in each time domain of the RHC. The simulation results verify the feasibility and effectiveness of the proposed method.
Keywords: Multi-UAV Formation; Receding horizon control (RHC); Pigeon-inspired optimization (PIO); Hierarchical topology.
Top-Down modulated model for object recognition in different categorization levels
by Fatemeh Sharifizadeh, Mohammad Ganjtabesh, Abbas Nowzari-Dalini
Abstract: The human visual system contains a hierarchical sequence of modules that take part in visual perception at superordinate, basic, and subordinate categorization levels. The top-down signals facilitate the bottom-up processing of visual information in the cortical analysis of object recognition. We propose a novel computational model for object recognition in different categorization levels, which mimics the effects of top- down signals in the hierarchical processing of the visual system. The top-down signal is incorporated in bottom-up processing of input image to increase the biological plausibility of our model as well as its efficiency for the object recognition in different categorization levels. The top-down signals provide a pre-knowledge about the input space, which can help to solve the complex object recognition tasks. The performance of our model is evaluated by various appraisal criteria with three benchmark datasets and significant improvement in recognition accuracy of our proposed model is achieved in all experiments.
Keywords: Object Recognition; Categorization Levels; Computational Models; Bottom-Up Processing; Top-Down Signals.
Inspiration-wise Swarm Intelligence Meta-heuristics for Continuous Optimization: A Survey--- Part III
by Nadia Nedjah, Luiza De Macedo Mourelle Mourelle, Reinaldo Gomes Moraes
Abstract: Optimization is an intrinsic part of many sciences such as engineering, logistics, transportation and economics among others. Global optimization techniques aim to find the best solution of a given a problem. In general, these problems are complex, nonlinear and intractable. On the other hand, there is a plethora of optimization techniques. Meta-heuristics are general optimization efficient tools. Swarm Intelligence is based on models inspired by swarming behaviors. The literature bears many swarm based meta-heuristics. In the first part of this survey, we propose an inspiration-wise taxonomy of such meta-heuristics and reviewed meta-heuristics that are guided by known human relation's characteristics and those that are inspired by physical system's properties. In the second part, we surveyed existing techniques that are inspired by fictional metaphors as well as flocking or schooling behaviors, which are considered to be bio-inspired. In this third part of the survey, we concentrate on bio-inspired methods that are guided by swarming, herding and proliferating behaviors. As an overall result of the survey, we point out the common inconvenience of using many of these meta-heuristics, which is related to the setting of the underlying parameters of the search strategy. Needless to state that their performance is dependent on the setting of these parameters. Nonetheless, there are some proposed techniques that reduce the required parameters to a minimum. Moreover, dynamic adjustment of these parameters during the search process is usually exploited to mitigate the impact of the parameter calibration process.
Keywords: Swarm Intelligence; Swarm-based Meta-heuristics; Bio-inspired Computation.
Classification of Diversified Web Crawler Accesses Inspired by Biological Adaptation
by Naomi Kuze, Shu Ishikura, Takeshi Yagi, Daiki Chiba, Masayuki Murata
Abstract: To discover and prevent attacks, it is necessary to collect data about the attacks using honeypots, and to identify malicious accesses from collected data. In this study, we focus on detecting a massive number of crawler accesses, which complicates the detection of malicious accesses. We adapt AntTree, a bio-inspired clustering scheme that is highly scalable and adaptable, for crawler detection. We also desgin a feature vector for crawler detection and propose a cluster interpretation method of AntTree. Our results show that the proposed bio-inspired mechanism can detect crawlers with a low false-negative rate, which is an advantage over conventional schemes for detecting various types of crawler.
Keywords: Network security; web vulnerability scanning detection; web honeypots; ant-based clustering.
Research on Interface Design Based on User\'s Mental Model Driven by Interactive Genetic Algorithm
by Zhen Wei, Jinghuan Nie
Abstract: This study proposes a computer interactive design method based on the analytic hierarchy process (AHP) and interactive genetic algorithm (IGA) to explore the mental model of user interface and promote the optimal design of interface. IGA is used in this study to display to users directly in the form of visual coding, and the most satisfied visual combination of users is obtained by iteration of user rating method. At the same time, in order to alleviate the problem of user fatigue and the problem that some design factors are not easy to be visual coded in IGA, AHP is introduced to obtain design factors user satisfied that are not easy to be visual coded, and the user preference is obtained by sorting the weight value of factors, then the user mental model is formed and the user mental model is transformed into design elements and integrate into visual design.
Keywords: user mental model; interactive genetic algorithm; analytic hierarchy process; interactive interface design.
Pigeon inspired optimization based cooperative target searching for multi-UAV in uncertain environment
by Delin Luo
Abstract: In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon inspired optimization (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilizing the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimized safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.
Keywords: multi-UAV; cooperative search; pigeon-inspired algorithm; artificial potential field.
Application an improved swarming optimization in Attribute Reduction
by Zhang Yi
Abstract: In order to solve the attribute reduction algorithms drawbacks of the extensive range of initial searching space and low convergence rate in the latter stage, we present an improved swarming optimization base on ant colony optimization in this paper. By strengthening the pheromone concentration of critical pipelines, the probability of the vital pipeline being selected in the path optimization process is increased, thereby improving the development of the optimal solution by the ant colony algorithm. The improved algorithm puts forward attributes of and adaptive choice model. The adaptive choice model enhances the possibility of choosing high-quality results? Simulation results show that the improved algorithms success rate converges to the optimal results. Moreover, the improved algorithm has the capability of high accuracy and fast convergence.
Keywords: swarming algorithm; hybrid; attribute reduction; optimisation.
Binary Fireworks Algorithm Application for Optimal Schedule of Electric Vehicle Reserve in Traditional and Restructured Electricity Markets
by Srikanth Reddy, Lokesh Panwar, B.K. Panigrahi, Rajesh Kumar
Abstract: The electric vehicle (EV) is a proven and reliable technology to curtail global emissions in the transportation sector. The multipurpose use of EV makes it a special entity not only in transportation sector but also in the modern smart grid. This paper presents an application of Binary Fireworks Algorithm for solving a multi-beneficiary model in which conventional thermal units are scheduled considering EV aggregator as an identical entity to GENCO in offering reserve services. The fast response of EV can be exploited in providing reserve capacity along with traditional unit commitment (UC) in regulated and price based unit commitment (PBUC) of thermal generators/units in deregulated electricity markets respectively. This paper extends the application of Binary Fireworks Algorithm (BFWA) to resource scheduling in smart grid to optimize the generation schedule of thermal and EV reserve in the day-ahead market. Also, develops first application of BFWA algorithm to solve PBUC. Thereafter, the resource scheduling objective in PBUC is extended to include EV reserve. Simulation results are presented for discussion and the same reveals that such reserve market participation of EV can improve the economics of both utility and EV. Also, compared to energy market participation, in reserve market profit for EV is increased by $3.59/MW and $3.96/MW in deregulated and traditional market scenario respectively. In addition, comparative analysis for two types of EV namely, battery electric vehicle (BEV), fuel cell electric vehicle (FCEV) is carried out with respect to cost-benefits. The market participation, especially reserve market participation proved much profitable for BEV compared to FCEV.
Keywords: Binary fireworks algorithm; Computational Intelligence; Unit Commitment; Profit Based unit Commitment (PBUC); Electric vehicle (EV); Electricity markets.
3D Reconstruction of Structural Magnetic Resonance Neuroimaging based on Computer Aided Detection
by Mai S. Mabrouk, Heba M. Afifi, Samir Y. Marzouk
Abstract: Computer Aided Detection (CAD) has numerous achievements in medical image processing field which represents a communication system between human recorded brain images and software for brain detection and diagnosis. CAD technology for brain images is the integration between scientists, engineers, and clinicians for analyzing brain images to help patients suffered from neuromuscular disorders, brain tumors, cerebral palsy, stroke, spinal cord injury, Epilepsy, Alzheimer and Parkinsons diseases. Magnetic resonance imaging (MRI) is an extensively used tool for discovery of neural diseases and determination of normal and abnormal brain images to support the radiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging, MRI has some restrictions related to human errors and incomplete interpretation of brain tumor regions. Also, MRI produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, computer-based classification is used to accurately distinguish between tumor regions from the brain MR images that provided early diagnosis and medical decision-making operation for brain diseases. This study investigated CAD system using 3D image reconstruction of MR brain and tumor structures efficiently. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SVM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model supported the advanced view of human brain diseases. Besides, accurate and quantitative research results are expected for enhancing the healthcare technology. This model will open a new framework for CAD technology to restore 3D brain images
Keywords: Computer Aided Detection (CAD); Magnetic resonance imaging (MRI); 3D image reconstruction; support vector machine (SVM); Fuzzy C-Means (FCM) algorithm.
An improved extension neural network methodology for fault diagnosis of complex electromechanical system
by Yunfei Zhou, Yunxiu Sai, Li Yan
Abstract: Fault diagnosis is a complex and challenging problem in the operation of machines and equipment, the crucial component of which is the recognition, extraction and classification of fault features, which are closely related to the training sets. A good diagnostic method can yield twice the result with half the effort result. In the existing intelligent diagnostic methods, artificial neural network (ANN) is widely used to identify and classify fault patterns. Although this approach solves the Fault diagnosis problem of complex electromechanical system (CES), there are still two defects: (1) The training process converges slowly; (2) Feature recognition and extraction are largely influenced by the training data sets. In order to solve the above problem, we propose a novel intelligent fault diagnosis method based on the extension neural network (ENN) and a uniform distribution search for particle swarm optimization (UPSO). The data set of the turbine generator set verifies the validity of this method. The diagnostic results show that compared with the existing method, the proposed method can not only extract the available classical domain characteristic from the collected training data set adaptively, but also have high diagnostic accuracy and rapid training process.
Keywords: fault diagnosis; complex electromechanical system; particle swarm optimization; extension neural network.
ELM-NeuralWalk: Trust evaluation for online social networks
by Shuoshuo Zhang, Xiangrong Tong, Shuigen Wang
Abstract: Trust relationship plays an important role in online shopping, recommendation systems, Internet of Things, etc. The problem of trust evaluation among users in online social network (OSN) has attracted much attention, and has become a hot issue in the domain of social computing. However, the way of trust propagation and aggregation in OSN is still not clear, as well as the accuracy of trust calculation. In order to calculate the indirect trust, an ELM-NeuralWalk algorithm to implement trust propagation and aggregation is proposed. ELM-WalkNet firstly learns two-hop trust calculation rules, calculates two-hop trust among users in the OSN. After that, ELM-NeuralWalk updates the OSN with the calculated trust value, so as to realize the calculation of multi-hop trust among users through iterative calling ELM-WalkNet. Unlike traditional solutions that use inference methods, ELM-WalkNet can learn trust calculation rules in an inductive way and accurately calculate indirect trust between users. Experiments on two real OSN datasets showed that ELM-NeuralWalk outperforms existing solutions.
Keywords: Trust; online social networks; extreme learning machine; machine learning.
Learning the Number of Filters in Convolutional Neural Networks
by Jue Li, Feng Cao, Honghong Cheng, Yuhua Qian
Abstract: Convolutional networks bring the performance of many computer vision tasks to unprecedented heights, but at the cost of enormous computation load. To reduce this cost, many model compression tasks have been proposed by eliminating insignificant model structures. For example, convolution filters with small absolute weights are pruned and then fine-tuned to restore reasonable accuracy. However, most of these works rely on pre-trained models without specific analysis of the changes in filters during the training process, resulting in sizable model retraining costs. Different from previous works, we interpret the change of filter behavior during training from the associated angle, and propose a novel filter pruning method utilizing the change rule, which can remove filters with similar functions later in training. According to this strategy, not only can we achieve model compression without fine-tuning, but we can also find a novel perspective to interpret the changing behavior of the filter during training. Moreover, our approach has been proved to be effective for many advanced CNN architectures.
Keywords: model compress; filter pruning; filter correlation; filter behavior interpretable.
Jumping particle swarm optimization method for solving minimum weight vertex cover problem
by Balaji Sankarasubramanian, Kanagasabapathy G, Vikram S. T.
Abstract: The minimum weight vertex cover problem (MWVCP) is one of the classic combinatorial optimization problem. In this paper we have proposed a modified jumping particle swarm optimization approach, named JPS-VC, for MWVCP. This method contains two new local search approaches incorporated with the jumping particle swarm optimization approach and it admits several remarkable features such as novel construction of feasible solution and also the quick conversion of feasible solution to optimal solution. The computed results indicate that the proposed approach has got appreciable performance over the recent state-of-the-art algorithms.
Keywords: minimum weight vertex cover; discrete particle swarm optimization; computational methods; combinatorial optimization.
Effect of potential well model for quantum heuristic algorithm: A comparative study and application
by Jin Jin, Peng Wang
Abstract: The multiscale quantum harmonic-oscillator algorithm (MQHOA) is an intelligent optimization algorithm based on quantum harmonic wave functions. Although it is effective for many optimization problems, an analysis for its performance is still lacking. This paper discusses the harmonic-oscillator potential well, delta-function potential well, and infinite-square potential well in terms of their application in evolutionary algorithms. Of the three, the harmonic-oscillator potential well is considered to give the most precise approximation for complex objective functions. To verify its global optimization performance, experiments are conducted using a suite of benchmark functions to compare the performance of different potential wells and heuristic algorithms. The experimental results indicate that MQHOA with the harmonic-oscillator potential well is a better practical choice than the other two potential well models, and show that MQHOA is a potential quantum heuristic algorithm.
Keywords: Multiscale quantum harmonic oscillator algorithm; quantum heuristic algorithm; global optimization; potential well; wave function.
A novel whale optimization algorithm with filtering disturbance and non-linear step
by Jinkun Luo, Fazhi He, Haoran Li, Xian-Tao Zeng, Yaqian Liang
Abstract: As a recent addition of population-based meta-heuristic algorithms, whale optimization algorithm (WOA) has attracted a lot of attention recently. However, WOA still has room for improvement in the accuracy and reliability of solution. In this study, a novel WOA with filtering disturbance and non-linear step (FDNS-WOA) is proposed. Firstly, to enhance the population diversity and the global search ability, we design a weighted Cauchy mutation equation to perturb solution space. Secondly, the excessive disturbance may cause the imbalance between exploration and exploitation, and the quality of solutions will be decreased. Thus, before the disturbance, we propose a filtering mechanism to dynamically select different individuals for disturbance at different evolutionary stages. Thirdly, a weighted local walk mechanism is designed to improve the local exploitation capability. Different from original linear step with large fluctuation, we propose a non-linear step to reduce blind spots in the local exploitation process. Finally, the proposed FDNS-WOA is tested on benchmark functions and applied in a real world problem. The experimental results show that the proposed FDNS-WOA not only outperforms other recent meta-heuristic algorithms in term of accuracy and reliability for most benchmark functions, but also achieves satisfactory results in real world problem.
Keywords: whale optimization algorithm; Cauchy mutation; economic load dispatch; meta-heuristic algorithm; population-based algorithm; filtering disturbance; non-linear step.
An adaptive multi-objective particle swarm optimization algorithm based on fitness distance to streamline repository
by Suyu Wang, Dengcheng Ma, Ze Ren, Yuanyuan Qu, Miao Wu
Abstract: In recent years, multi-objective particle swarm optimization algorithm (MOPSO) has been paid more attention. One of its indispensable structures is the maintenance and update mechanism of the repository. The existing mechanisms are relatively simple, and most of them are based on the crowding distance sorting strategy, and not conducive to the distribution and accuracy of the algorithms. The paper innovated this mechanism and proposed an adaptive multi-objective particle swarm optimization algorithm to streamline repository based on fitness distance (FDMOPSO). Both the concept of fitness distance and the corresponding improve methods of mutation mechanism and adaptive mechanism were proposed. The algorithm itself was tested using benchmarks. The results shown that the proposed application of fitness distance had a better improvement on the convergence and distribution. Compared with other algorithms, the FDMOPSO algorithm had the best overall performance.
Keywords: MOPSO; fitness distance; streamline repository; adaptive; multi-objective optimization.
Efficient Optimization Methods in a Distributed Memory Storage System Using Data Compression
by Xiaoyang Yu, Songfu Lu, Tongyang Wang, Xinfang Zhang, Shaohua Wan
Abstract: The storage of data in a memory-centric storage system benefits from great improvements in read and write I/Ornperformance at the price of consuming a large amount of valuable memory resources. As the amount of data grows, a distributedrnsystem must scale accordingly, resulting in an increased number of nodes and greater communication overhead that may impact thernoverall performance. We believe that achieving an appropriate trade-off among computation, storage and network transportation will bernbeneficial for a distributed memory storage system, leading to higher overall efficiency. The challenges of achieving such a trade-off arerndetermining how and when this trade-off should be applied. In this paper, we explore a method to achieve this trade-off by introducingrndata compression technology in a transparent manner. Instead of focusing on specific compressed data structures, we target blockrnlevel compression for a general purpose storage system to incorporate a wide range of existing data analysis frameworks and usagernscenarios, especially with big data. A prototype is implemented and evaluated based on the memory-centric distributed storage systemrnAlluxio to provide transparent compression and decompression during write/read operations. A series of experiments for data withrndifferent types of compression ratio is conducted to determine the proper situation in which this technology can help to improve thernsystem efficiency. The experimental results prove that our approach can achieve huge write/read throughput boosts for highlyrncompressible data and performs better for moderately compressible data when there are remote operations. From the results, wernsuggest that our approach can be applied for data with a medium up to high compression ratio in practical data processing pipelinesrnand provides useful insights for optimizing future memory-based storage systems.
Keywords: System performance optimization; Data compression; Distributed memory storage.