A New Expansion of Cooperative Particle Swarm Optimization

Abstract

We previously proposed multiple particle swarm optimizers with diversive curiosity (MPSOα/DC). Its main features are to introduce diversive curiosity and localized random search into MPSO to comprehensively manage the trade-off between exploitation and exploration for preventing stagnation and improving the search efficiency. In this paper, we further extend these features to multiple particle swarm optimizers with inertia weight and multiple canonical particle swarm optimizers to create two analogues, called MPSOIWα/DC and MCPSOα/DC. To demonstrate the effectiveness of these proposals, computer experiments on a suite of multidimensional benchmark problems are carried out. The obtained results show that the search performance of the MPSOα/DC is superior to that of both the MPSOIWα/DC and MCPSOα/DC, and they have better search efficiency compared to other methods such as the convenient cooperative PSO and a real-coded genetic algorithm.