Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization

Authors:
Masao Iwamatsu
Affiliations:
The author is with the Faculty of Engineering, Musashi Institute of Technology, Tokyo, 158--8557 Japan. E-mail: iwamatsu@ph.ns.musashi-tech.ac.jp
Venue:
IEICE - Transactions on Information and Systems
Year:
2006

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Abstract

This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.