Variance Priority based Cooperative Co-evolution Differential Evolution for Large Scale Global Optimization

Large scale global optimization (LSGO) is a very important and extremely difficult task in optimization domain, which is urgently needed for scientific and engineering applications. Recently, decompose-and-conquer strategy has become a promising method to handle LSGO problems. In this paper, we propose a new strategy variance priority (V-P) to improve the classical cooperative co-evolution framework. Based on this proposed strategy, a new LSGO algorithm, variance priority based cooperative co-evolution differential evolution (VP-DECC), is developed. The advantages of VP strategy over the other decompose-and-conquer strategies are experimentally investigated. Especially, it has shown excellent performance in dealing with more complex problems.