A Newly Cooperative PSO – Multiple Particle Swarm Optimizers with Diversive Curiosity, MPSO α /DC

In this paper we propose a newly multiple particle swarm optimizers with diversive curiosity (MPSOα/DC) for enhancing the search performance. It has three outstanding features: (1) Implementing plural particle swarms in parallel to explore; (2) Finding the most suitable solution in a small limited space by a localized random search for correcting the solution found by each particle swarm; (3) Introducing diversive curiosity into the multi-swarm to alleviate stagnation. To demonstrate the proposal’s effectiveness, computer experiments on a suite of benchmark problems are carried out. We investigate its intrinsic characteristics, and compare the search performance with other methods. The obtained results show that the search performance of the MPSOα/DC is superior to that by the PSO/DC, EPSO, OPSO, and RGA/E for the given benchmark problems.

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