An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems

Authors:
Ignacio G. del Amo;David A. Pelta;Juan R. González;Pavel Novoa
Affiliations:
Models of Decision and Optimization, Dept. of Computer Sciences and Artificial Intelligence, University of Granada;Models of Decision and Optimization, Dept. of Computer Sciences and Artificial Intelligence, University of Granada;Models of Decision and Optimization, Dept. of Computer Sciences and Artificial Intelligence, University of Granada;University of Holguín, Holguín, Cuba
Venue:
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Year:
2009

Citing 7
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Abstract

The particle swarm optimization (PSO) algorithm has successfully been applied to dynamic optimization problems with very competitive results. One of its best performing variants is the one based on the atomic model, with quantum and trajectory particles. However, there is no precise knowledge on how these particles contribute to the global behavior of the swarms during the optimization process. This work analyzes several aspects of each type of particle, including the best combination of them for different scenarios, and how many times do they contribute to the swarm's best. Results show that, for the Moving Peaks Benchmark (MPB), a higher number of trajectory particles than quantum particles is the best strategy. Quantum particles are most helpful immediately after a change in the environment has occurred, while trajectory particles lead the optimization in the final stages. Suggestions on how to use this knowledge for future developments are also provided.