A Review and Empirical Analysis of Particle Swarm optimization Algorithms for Dynamic Multi-Modal optimization

A number of particle swarm optimization (PSO) variations have been developed to find multiple solutions to multimodal optimization problems. These algorithms have been extensively evaluated in the literature. When dynamic optimization problems are considered, only a few PSO algorithms exist that have the ability to find and track multiple optima in dynamically changing search landscapes. These algorithms have not yet been rigorously evaluated on an extensive set of dynamic optimization problems. This paper presents a review of existing dynamic multimodal PSO algorithms and conducts an empirical analysis of these algorithms on a set of dynamic optimization problems of varying dynamics. The best performing dynamic multi-modal PSO algorithms, with respect to different performance measures, are identified as an outcome of a formal statistical analysis.

[1]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[2]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[3]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[4]  Andries Petrus Engelbrecht,et al.  Niching for Dynamic Environments Using Particle Swarm Optimization , 2006, SEAL.

[5]  Xiaodong Li,et al.  Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications , 2017, IEEE Transactions on Evolutionary Computation.

[6]  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 .

[7]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

[8]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[9]  Rolf Wanka,et al.  Theoretical Analysis of Initial Particle Swarm Behavior , 2008, PPSN.

[10]  S. Kiranyaz,et al.  Dynamic Multi-swarm Particle Swarm Optimization with Fractional Global Best Formation , 2008 .

[11]  Andries Petrus Engelbrecht,et al.  Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm , 2018, Swarm Evol. Comput..

[12]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[13]  Ofer M. Shir,et al.  Niching in Evolutionary Algorithms , 2012, Handbook of Natural Computing.

[14]  Mike Preuss,et al.  Multimodal Optimization by Means of Evolutionary Algorithms , 2015, Natural Computing Series.

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Andries Petrus Engelbrecht,et al.  Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima , 2013, Metaheuristics for Dynamic Optimization.

[17]  Andries Petrus Engelbrecht,et al.  A novel particle swarm niching technique based on extensive vector operations , 2010, Natural Computing.

[18]  Ming Yang,et al.  An Adaptive Multi-Swarm Optimizer for Dynamic Optimization Problems , 2014, Evolutionary Computation.

[19]  Andries Petrus Engelbrecht,et al.  On the optimality of particle swarm parameters in dynamic environments , 2013, 2013 IEEE Congress on Evolutionary Computation.

[20]  Shengxiang Yang,et al.  A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems , 2012, Int. J. Syst. Sci..

[21]  A. P. Engelbrecht.  Roaming Behavior of Unconstrained Particles , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

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

[23]  Jürgen Branke,et al.  Multiswarms, exclusion, and anti-convergence in dynamic environments , 2006, IEEE Transactions on Evolutionary Computation.