PSO-based multiple optima search systems with switched topology

This paper discusses a particle swarm optimization (PSO) with switched topology and its application to the multi-solution problems. First, we introduce a deterministic PSO characterized by normalized deterministic parameters and a canonical form system equation. This system is convenient to grasp effects of parameters on the stability. Second, we investigate effects of the average distance of several the swarm topologies on the search capability. Especially, we introduce the switched topology where any information is not transmitted from the edge if the switch is off. Third, we consider an application to exploring multiple periodic points in simple dynamical systems. Performing numerical experiments for typical examples, the algorithm performance is investigated.

[1]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[2]  Xiaodong Li,et al.  Erratum to "Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology" [Feb 10 150-169] , 2010, IEEE Trans. Evol. Comput..

[3]  Kenya Jin'no.  A novel deterministic particle swarm optimization system (Special issue on nonlinear circuits and signal processing) , 2009 .

[4]  Toshimichi Saito,et al.  Application of particle swarm optimization to parameter search in dynamical systems , 2011 .

[5]  Kenya Jin'no,et al.  The neighborhood of canonical deterministic PSO , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

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

[7]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.

[8]  Jin’No Kenya.  新しい決定論的粒子群最適化システム | 文献情報 | J-GLOBAL 科学技術総合リンクセンター , 2009 .

[9]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[10]  Kenya Jin'no,et al.  Analysis of dynamical characteristic of canonical deterministic PSO , 2010, IEEE Congress on Evolutionary Computation.

[11]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[12]  Jonathan W. Kimball,et al.  Particle swarm optimization of high-frequency transformer , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[13]  Visakan Kadirkamanathan,et al.  Stability analysis of the particle dynamics in particle swarm optimizer , 2006, IEEE Transactions on Evolutionary Computation.

[14]  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 .

[15]  Xiaodong Li,et al.  Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology , 2010, IEEE Transactions on Evolutionary Computation.

[16]  Toshimichi Saito,et al.  A Discrete Particle Swarm Optimizer for Multi-Solution Problems , 2012, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[17]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[18]  Russell C. Eberhart,et al.  chapter seven – The Particle Swarm , 2001 .