A Comparative Study of Four Parallel and Distributed PSO Methods

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23 Citations (Scopus)


We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an "island model"-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. We study the proposed methods on a wide set of problems including theoretically hand-tailored benchmarks and complex real-life applications from the field of drug discovery, with a particular focus on the generalization ability of the obtained solutions. We show that the proposed repulsive multi-swarm system has a better optimization ability than all the other presented methods on all the studied problems. Interestingly, the proposed repulsive multi-swarm system is also the one that returns the most general solutions.
Original languageUnknown
Pages (from-to)129-161
JournalNew Generation Computing
Issue number2
Publication statusPublished - 1 Jan 2011

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