Nesting Discrete Particle Swarm Optimizers for Multi-solution Problems

This paper studies a discrete particle swarm optimizer for multi-solution problems. The algorithm consists of two stages. The first stage is global search: the whole search space is discretized into the local sub-regions each of which has one approximate solution. The sub-region consists of subsets of lattice points in relatively rough resolution. The second stage is local search. Each subregion is re-discretized into finer lattice points and the algorithm operates in all the subregions in parallel to find all approximate solutions. Performing basic numerical experiment, the algorithm efficiency is investigated.

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

[2]  Toshimichi Saito,et al.  Design of switching circuits based on particle swarm optimizer and hybrid fitness function , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[3]  Juan Humberto Sossa Azuela,et al.  Design of artificial neural networks using a modified Particle Swarm Optimization algorithm , 2009, 2009 International Joint Conference on Neural Networks.

[4]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

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

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

[7]  Jacek M. Zurada,et al.  An approach to multimodal biomedical image registration utilizing particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Toshimichi Saito,et al.  Particle Swarm Optimizers with Growing Tree Topology , 2009, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[9]  Xin-She Yang,et al.  Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization , 2010, NICSO.

[10]  Fatih Erdogan Sevilgen,et al.  Discrete Particle Swarm Optimization for the Orienteering Problem , 2010, IEEE Congress on Evolutionary Computation.