关键词: 粒子群优化算法, 搜索能力, 局部最优, 自适应策略

Abstract: In order to solve the poor local search ability of the particle population, a multi-objective particle swarm optimization algorithm based on adaptive population updating strategy is proposed. At each population iteration, according to the population diversity measure and each particle’s fitness value, the algorithm changes the speed weight adaptively to improve the local search activity of particle population, and the algorithm retains enough global search ability. Finally, multiple groups of classical test samples are used for simulation, compared with the traditional particle swarm algorithm and speed linear attenuation algorithm. In single objective optimization, adaptive particle swarm optimization algorithm can find the optimal location faster. In multi-objective optimization, adaptive particle swarm optimization algorithm can convergence to Pareto optimal boundary more quickly.

Key words: particle swarm optimization, search capabilities, local optimal, adaptive strategy