Controlling Particle Trajectories in a Multi-swarm Approach for Dynamic Optimization Problems

Authors:
Pavel Novoa;David A. Pelta;Carlos Cruz;Ignacio García Amo
Affiliations:
University of Holguín, Holguín, Cuba;Models of Decision and Optimization Research Group, University of Granada, Granada, Spain 18071;Models of Decision and Optimization Research Group, University of Granada, Granada, Spain 18071;Models of Decision and Optimization Research Group, University of Granada, Granada, Spain 18071
Venue:
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
Year:
2009

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Abstract

In recent years, particle swarm optimization has emerged as a suitable optimization technique for dynamic environments, mainly its multi-swarm variant. However, in the search for good solutions some particles may produce transitions between non improving ones. Although this fact is usual in stochastic algorithms like PSO, when the problem at hand is dynamic in some sense one can consider that those particles are wasting resources (evaluations, time, etc). To overcome this problem, a novel operator for controlling particle trajectories is introduced into a multi-swarm PSO algorithm. Experimental studies over a benchmark problem shows the benefits of the proposal.