In order to improve the convergence and distribution of Many-Objective Evolutionary Algorithms (MOEAs)

this paper proposes a Many-Objective Fuzzy Evolutionary Algorithm (MFEA) which is based on fuzzy dominance.On the model of algorithms based on Pareto-dominance

we improve the environmental selection using fuzzy logic.We present a new dominance strategy based on fuzzy membership.Then

we propose a new estimation method of crowding distance which incorporates Harmonic-distance

k-neighborhood method and niche technique.Finally

according to the characteristics of MOPs and the idea of -cut set

we design a new environmental selection strategy which is made up of two truncations.The proposed algorithm is compared to 5 state-of-the-art MOEAs on benchmark test problems.Simulation results show that -MFEA has obvious advantages than other algorithms because MFEA could ensure good convergence while has uniform distribution

especially

applied to solving high-dimensional MOPs.