关键词: 多目标进化算法, 收敛性, 多样性, 干扰集, Pareto等级

Abstract: A hybrid multi-objective evolutionary algorithm based on Pareto ranking called PHMOEA is proposed to improve the convergence and diversity of the solution sets in the multi-objective evolutionary algorithm. The algorithm defines interference sets in order to stimulate the composition of the non-dominating sets, meanwhile, improves the crossover operator and mutation operator based on the Pareto sort and the strategy of elite retention. It evaluates PHMOEA with thirteen standard benchmark problems, and is compared with two state-of-the-art multi-objective optimizers, NSGA-II and SPEA2. The results obtained indicate that PHMOEA has good diversity and convergence, what’s more, remains a better uniformity distribution and broader coverage.

Key words: multi-objective evolutionary, convergence, diversity, interference sets, Pareto sort