Multiple Particle Swarm Optimizers with Inertia Weight with Diversive Curiosity and Its Performance Test

This paper presents a new method of curiosity-driven multi-swarm search, called multiple particle swarm optimizers with inertia weight with diversive curiosity (MPSOIWα/DC). Compared to a plain MPSOIW, it has the following outstanding features: (1) Decentralization in multi-swarm exploration with hybrid search, (2) Concentration in evaluation and behavior control with diversive curiosity, (3) Practical use of the results of evolutionary PSOIW, and (4) Their effective combination. This achievement expands the applied object of cooperative PSO with the multi-swarm’s decision-making. To demonstrate the effectiveness of the proposal, computer experiments on a suite of multidimensional benchmark problems are carried out. We examine the intrinsic characteristics of the proposal, and compare the search performance with other methods. The obtained experimental results clearly indicate that the search performance of the MPSOIWα/DC is superior to that by the EPSOIW, PSOIW, OPSO, RGA/E, and MPSOα/DC for the given benchmark problems.

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