Particle Swarm Optimization for Multimodal Functions:  A Clustering Approach

Passaro, Alessandro; Starita, Antonina

doi:https://doi.org/10.1155/2008/482032

Journal of Artificial Evolution and Applications

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Particle Swarms: The Second Decade

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Research Article | Open Access

Volume 2008 | Article ID 482032 | https://doi.org/10.1155/2008/482032

Particle Swarm Optimization for Multimodal Functions: A Clustering Approach

Alessandro Passaro¹and Antonina Starita¹

Academic Editor: Riccardo Poli

Received13 Jul 2007

Revised18 Dec 2007

Accepted08 Feb 2008

Published24 Apr 2008

Abstract

The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework of niching. In this work, we present a new approach to niching in PSO based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to locate multiple optima in parallel. Our approach was implemented in the k-means-based PSO (kPSO), which employs the standard k-means clustering algorithm, improved with a mechanism to adaptively identify the number of clusters. kPSO proved to be a competitive solution when compared with other existing algorithms, since it showed better performance on a benchmark set of multimodal functions.

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Copyright

Copyright © 2008 Alessandro Passaro and Antonina Starita. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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