An analysis of multiple particle swarm optimizers with inertia weight with diversive curiosity

In this paper we present a newly multiple particle swarm optimizers with inertia weight with diversive curiosity (MPSOIWα/DC) for improving the search performance and intelligent processing of a plain MPSOIW. It has the follow ing 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 co operative PSO, and develops the approach of the curiosity-driven multi-swarm. To demonstrate the effectiveness of the proposal, computer experiments on a suite of multidimensional benchmark problems are carried out to analytical judgment. We examine its intrinsic characteristics, and compare the search performance with other methods. The obtained experimental results indicate that the search performance of the MPSOIWα/DC is superior to that by the PSOIW/DC, EPSOIW, PSOIW, OPSO, RGA/E, and MPSOα/DC for the given benchmark problems.