Abstract: In order to improve the convergence and diversity of the multi-objective differential evolution algorithm in solving multi-objective optimization problems,this paper proposed a multi-objective differential evolution algorithm with fuzzy adaptive ranking-based mutation.Firstly,the global exploration and the local exploitation are balanced by using the fuzzy system which adaptively adjust the parameters of ranking-based mutation,so the convergence rate of the algorithm is accelerated and the possibility of the algorithm falling into a local optimum is reduced.Secondly,for the sake of improving the stability and diversity of the algorithm,an initial population with good diversity is obtained through the uniform population initialization method at the beginning of the algorithm.Finally,the discarded individuals is stored by adding them to a temporary population for the final selection in the end of each iteration,therefore,the population diversity during the evolution process is improved.Simulation experiments were conducted on the seven standard test functions and three test functions with bias features.The experimental results show that compared with other four algorithms,the proposed algorithm has better convergence and diversity,and it can effectively approach to the real Pareto frontier.The effectiveness of the fuzzy adaptive ranking-based mutation strategy in the proposed algorithm is also verified by experimental comparison method.

Key words: Adaptive strategy, Differential evolution, Fuzzy system, Multi-objective optimization problem, Ranking-based mutation