Expansion of Particle Multi-Swarm Optimization

Hiroshi Sho


For improving the search ability and performance of elementary multiple particle swarm optimizers, we, in this paper, propose a series of multiple particle swarm optimizers with information sharing by introducing a special strategy,called multi-swarm information sharing. The key idea, here, is to add a special confidence term into the updating rule of the particle's velocity by the best solution found out by the particle multi-swarm search. This is a new type approach for the technical development and evolution of particle multi-swarm optimization itself. In order to confirm the effectiveness of the information sharing strategy in the proposed particle multi-swarm search, several computer experiments of dealing with a suite of benchmark problems are carried out. For investigating the performance and efficiency of these proposed methods, we compare their search ability and performance, respectively. The obtained experimental results show that the proposed methods have better search ability and performance than those methods without the strategy. And we still decide the best value of adding the new confidence coefficient to the multi-swarm for dealing with the given optimization problems.

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DOI: https://doi.org/10.5430/air.v7n2p74


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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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