Particle Swarm Optimization with a Novel Multi-Parent Crossover Operator
Hui Wang, Zhijian Wu, Yong Liu, Sanyou Y. Zeng · 2008
Particle swarm optimization (PSO) shares many similarities with evolutionary algorithms (EAs), while the standard PSO does not use any evolution operators such as crossover and mutation. This paper presents a hybrid PSO algorithm to inherit some excellent characteristics of advanced evolutionary computation techniques. The proposed method employs a novel multi-parent crossover operator and a self-adaptive Cauchy mutation operator to help escape from local optima. Experimental results on a suit of well-known benchmark functions with many local minima have shown that the proposed method could successfully deal with those difficult multimodal optimization problems.