Species based evolutionary algorithms for multimodal optimization: A brief review

The species conservation technique is a relatively new approach to finding multiple solutions of a multimodal optimization problem. When adopting such a technique, a species is defined as a group of individuals in a population that have similar characteristics and are dominated by the best individual, called the species seed. Species conservation techniques are used to identify species within a population and to conserve the identified species in the current generation. A ‘species-based evolutionary algorithm’ (SEA) is the combination of a species conservation technique with an evolutionary algorithm, such as genetic algorithms, particle swarm optimization, or differential evolution. These SEAs have been demonstrated to be effective in searching multiple solutions of a multimodal optimization problem. This paper will briefly review its principles and its variants developed to date. These methods had been used to solve engineering optimization problems and found some new solutions.

[1]  Hyun-Kyo Jung,et al.  A novel algorithm for multimodal function optimization based on evolution strategy , 2004, IEEE Transactions on Magnetics.

[2]  Jing Peng,et al.  Particle Swarm Optimization with Multiscale Searching Method , 2005, CIS.

[3]  Patrick Siarry,et al.  Island Model Cooperating with Speciation for Multimodal Optimization , 2000, PPSN.

[4]  P. John Clarkson,et al.  Multimodal Function Optimization Using Species Conservation , 2001 .

[5]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[6]  Tetsuyuki Takahama,et al.  Species-based differential evolution with switching search strategies for multimodal function optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Geoffrey T. Parks,et al.  Engineering design optimization using species-conserving genetic algorithms , 2007 .

[8]  P. John Clarkson,et al.  Complexity analysis of evaluating distances in the genetic algorithm using species conservation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[9]  Ender Özcan,et al.  Particle Swarms for Multimodal Optimization , 2007, ICANNGA.

[10]  Jian-Ping Li,et al.  Species-conserving particle swarm optimisation for multimodal functions , 2009, Int. J. Model. Identif. Control..

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Xiaodong Li,et al.  A particle swarm model for tracking multiple peaks in a dynamic environment using speciation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[13]  Xiaodong Li,et al.  Efficient differential evolution using speciation for multimodal function optimization , 2005, GECCO '05.

[14]  Xiaodong Li,et al.  Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization , 2004, GECCO.

[15]  Jian-Ping Li,et al.  An adaptive species conservation genetic algorithm for multimodal optimization , 2009 .

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

[17]  Dumitru Dumitrescu,et al.  Disburdening the species conservation evolutionary algorithm of arguing with radii , 2007, GECCO '07.

[18]  P. John Clarkson,et al.  A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2002, Evolutionary Computation.

[19]  Chang-Hwan Im,et al.  A novel algorithm for multimodal function optimization based on evolution strategy , 2004 .

[20]  I. C. Parmee.  A Review of the Development and Application of Cluster Oriented Genetic Algorithms , 2004 .

[21]  P. John Clarkson,et al.  Erratum: A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2003, Evolutionary Computation.

[22]  Li Jianping,et al.  The effect of distance measure in a GA with species conservation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[23]  Wei Hou,et al.  A Mixed Mutation Strategy Evolutionary Programming Combined with Species Conservation Technique , 2005, MICAI.

[24]  Jie Yao,et al.  On Clustering in Evolutionary Computation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[25]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[26]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[27]  Rasmus K. Ursem,et al.  Multinational GAs: Multimodal Optimization Techniques in Dynamic Environments , 2000, GECCO.

[28]  R. K. Ursem.  Multinational evolutionary algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[29]  Pedro J. Ballester,et al.  Characterising the parameter space of a highly nonlinear inverse problem , 2006 .

[30]  Shigenobu Kobayashi,et al.  Adaptive isolation model using data clustering for multimodal function optimization , 2005, GECCO '05.

[31]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[32]  Xiaodong Li,et al.  Adaptively choosing niching parameters in a PSO , 2006, GECCO.

[33]  Chunshien Li,et al.  Optimization by random search with jumps , 2004 .

[34]  Ole J Mengshoel,et al.  The Crowding Approach to Niching in Genetic Algorithms , 2008, Evolutionary Computation.

[35]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[36]  Bin Wu,et al.  A detecting peak's number technique for multimodal function optimization , 2007 .

[37]  Xiaodong Li,et al.  Particle swarm with speciation and adaptation in a dynamic environment , 2006, GECCO.

[38]  Antonina Starita,et al.  Particle swarm optimization for multimodal functions: a clustering approach , 2008 .

[39]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[40]  R. Eberhart,et al.  Particle Swarm Optimization-Neural Networks, 1995. Proceedings., IEEE International Conference on , 2004 .

[41]  Masao Iwamatsu.  Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization , 2006, IEICE Trans. Inf. Syst..

[42]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[43]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[44]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.