A new method of cooperative PSO: Multiple particle swarm optimizers with inertia weight with diversive curiosity

Abstract

This chapter presents a new method of cooperative PSO-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 (DC), (3) Practical use of the results of an evolutionary PSOIW, and (4) Their effective combination. The actualization of its overall composition expands the applied object of cooperative PSO, and effectually alleviates stagnation in optimization with the multi-swarm's decision-making. To demonstrate the effectiveness of the proposal, computer experiments on a suite of multi-dimensional benchmark problems are carried out. We examine its intrinsic characteristics, 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 of the PSOIW, OPSO, and RGA/E, and is better than that of the MPSOα/DC, and MCPSOα/DC except for the Rosenbrock Problem. © 2012 Springer Science+Business Media, LLC.