Multi-DEPSO: A DE and PSO based hybrid algorithm in dynamic environments

Li Xiao, Xingquan Zuo

Abstract: A new hybrid algorithm based on Differential Evolution (DE) and Particle Swarm Optimization (PSO) is proposed in this paper for dynamic optimization problems. The multi-population strategy is used to enhance the diversity and keeps each subpopulation on a different peak, and then a hybrid operator based on DE and PSO (DEPSO) is designed to find and track the optima for each subpopulation. Using DEPSO operator, each individual in subpopulations is sequentially carried out DE and PSO operations. An exclusion scheme is proposed which integrates the distance based exclusion scheme with hill-valley function. The algorithm is applied to Moving Peaks Benchmark (MPB) problem. Experimental results show that it is significantly better in terms of averaged offline error than other state-of-the-art algorithms.