关键词: 粒子群，距离度量，惯性权值，模糊集，隶属函数

Abstract: The classical Particle Swarm Optimization (PSO) neglects the difference among particles while updating a particle's velocity in a generation. I}o cope with this issue, a novel Distanccbased Adaptive Fuzzy Particle Swarm Optimization (DAFPSO) was proposed in this paper. The DAFPSO designed membership functions to tune the basic parameters used in updating a particle's velocity according to the distance between the current particle and the global best particle. Several classical benchmark functions were used to evaluate the (DAFPSO.The experiments demonstrate the efficiency and effectiveness of the proposed DAFPSO.

Key words: PSO, Distance measurement, Inertia weight, Fuzzy set, Membership function