A novel particle swarms with mixed cooperative co-evolution for large scale global optimisation

Abstract

Identification of variable interaction and grouping of variables plays an important role in the divide-and-conquer algorithm. In this paper, a novel particle swarm optimisation with mixed cooperative co-evolution (MCCPSO) is proposed. It has two strategies and one mechanism: mixed grouping of variables (MGV) strategy, reallocate computational resources (RCR) strategy and a competitive leadership with a lifecycle mechanism. MGV can effectively identify the direct and indirect interactive variables and form a spare sub-group pool. RCR can give more computational resources to the more important subcomponents. The leader mechanism can prevent the PSO algorithm from falling into a local optimum. In order to understand the characteristics of MCCPSO, we have carried out extensive computational studies on the CEC'2010 benchmark function. The experimental results show that the performance of MCCPSO is better than the other four state-of-the-art algorithms.