关键词: 粒子群算法, 概率分配, 自适应调整, 优化

Abstract: After analyzing convergence and diversity of the multi-objective Particle Swarm Optimization （PSO）, an improved multi-objective PSO, which is introduced the method of probability assignment, is proposed. The arithmetic calculates the fitness of the particles basis of the dominative rank using the Pareto sorting. In order to increase the diversity of the optimal solution, a penalty function including the crowding distance is added to the fitness. Then the best particle is selected according to the probability comparing. Also the inertia coefficients are adjusted adaptively based on the information of the particles and the iterative number performances. So the convergence rate of the arithmetic can be accelerated. At last the validity of the arithmetic is validated via the testing function.

Key words: particle swarm optimization, probability assignment, adaptive adjusting, optimal