Composite Particle Swarm Optimizer With Historical Memory for Function Optimization

Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

[1]  Bin Jiao,et al.  A Self-Government Particle Swarm Optimization Algorithm and Its Application in Texaco Gasification , 2013, J. Softw..

[2]  Jinung An,et al.  Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs , 2012, Inf. Sci..

[3]  MengChu Zhou,et al.  An adaptive particle swarm optimization method based on clustering , 2015, Soft Comput..

[4]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[5]  Jing Zhang,et al.  Coevolutionary Particle Swarm Optimization Using AIS and its Application in Multiparameter Estimation of PMSM , 2013, IEEE Transactions on Cybernetics.

[6]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Liang Gao,et al.  Cellular particle swarm optimization , 2011, Inf. Sci..

[8]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[9]  Qingfu Zhang,et al.  Meta-Heuristic Combining Prior Online and Offline Information for the Quadratic Assignment Problem , 2014, IEEE Transactions on Cybernetics.

[10]  Alexander Mendiburu,et al.  A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems , 2012, Progress in Artificial Intelligence.

[11]  Yuhui Shi,et al.  ?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration , 2013, IEEE Computational Intelligence Magazine.

[12]  Jiahai Wang,et al.  Genetic Particle Swarm Optimization Based on Estimation of Distribution , 2007, LSMS.

[13]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[14]  Mauro Brunato,et al.  R-EVO: A Reactive Evolutionary Algorithm for the Maximum Clique Problem , 2011, IEEE Transactions on Evolutionary Computation.

[15]  Bin Li,et al.  Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  Marco Antonio Montes de Oca,et al.  An Estimation of Distribution Particle Swarm Optimization Algorithm , 2006, ANTS Workshop.

[17]  Yalan Zhou,et al.  A Discrete Estimation of Distribution Particle Swarm Optimization for Combinatorial Optimization Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[18]  Athanasios V. Vasilakos,et al.  Improved CMA-ES with Memory based Directed Individual Generation for Real Parameter Optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[19]  Feng Chu,et al.  Recent Advances and Issues in Facility Location Problems , 2015 .

[20]  Ramiz M. Aliguliyev,et al.  An Optimization Model and DPSO-EDA for Document Summarization , 2011 .

[21]  MengChu Zhou,et al.  Swarm Intelligence Approaches to Optimal Power Flow Problem With Distributed Generator Failures in Power Networks , 2013, IEEE Transactions on Automation Science and Engineering.

[22]  Liang Gao,et al.  A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem , 2011, Expert Syst. Appl..

[23]  Ying Lin,et al.  Particle Swarm Optimization With an Aging Leader and Challengers , 2013, IEEE Transactions on Evolutionary Computation.

[24]  Christopher Stone,et al.  Evolution of cellular automata with memory: The Density Classification Task , 2009, Biosyst..

[25]  Pedro Larrañaga,et al.  EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization , 2010, ANTS Conference.

[26]  Sadiq M. Sait,et al.  A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem , 2014, Appl. Soft Comput..

[27]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[28]  MengChu Zhou,et al.  Deadlock-Free Genetic Scheduling Algorithm for Automated Manufacturing Systems Based on Deadlock Control Policy , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[30]  Mohammed El-Abd.  Preventing premature convergence in a PSO and EDA hybrid , 2009, 2009 IEEE Congress on Evolutionary Computation.

[31]  Shengxiang Yang,et al.  Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments , 2011, Soft Comput..

[32]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[33]  M. Zhou,et al.  Gaussian Classifier-Based Evolutionary Strategy for Multimodal Optimization , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Yalan Zhou,et al.  Discrete particle swarm optimization based on estimation of distribution for polygonal approximation problems , 2009, Expert Syst. Appl..

[35]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[36]  Liangpei Zhang,et al.  Sub-pixel mapping based on artificial immune systems for remote sensing imagery , 2013, Pattern Recognit..

[37]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[38]  Kenli Li,et al.  Memory does not necessarily promote cooperation in dilemma games , 2014 .

[39]  Yunong Zhang,et al.  Discrete quantum-behaved particle swarm optimization based on estimation of distribution for combinatorial optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[40]  Vahid Vahidinasab,et al.  A modified harmony search method for environmental/economic load dispatch of real-world power systems , 2014 .

[41]  Alfredo Milani,et al.  Particle Swarm Optimization in the EDAs Framework , 2011 .

[42]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[43]  M. R. Meybodi,et al.  Memory/search RCLA-EC: A CLA-EC for moving parabola problem , 2011, 2011 6th International Conference on Computer Sciences and Convergence Information Technology (ICCIT).

[44]  Sadiq M. Sait,et al.  Finding Multi-Objective Shortest Paths Using Memory-Efficient Stochastic Evolution Based Algorithm , 2012, 2012 Third International Conference on Networking and Computing.

[45]  Mohammed El-Abd,et al.  PSO_Bounds: A New Hybridization Technique of PSO and EDAs , 2009, Foundations of Computational Intelligence.

[46]  Tao Song,et al.  A membrane-inspired algorithm with a memory mechanism for knapsack problems , 2013, Journal of Zhejiang University SCIENCE C.