关键词: 粒子群算法, 优化, 适应度, 惯性权重, 变异

Abstract:

Taking into account various influencing factors， this study established a logistics distribution center location model with the least total cost and the best timeliness. A particle swarm optimization with variation and dynamic self-adjustment is proposed to solve the complex practical optimization problem. The parameters in the algorithm are adjusted dynamically and non-linearly. At the same time， the variation mechanism with transplanted genetic algorithm helps the algorithm effectively get rid of the constraints of local optimization. The results show that the particle swarm optimization based on dual mechanism optimization has further improved the algorithm efficiency and optimization ability. The proposed algorithm is applied to the solution of the more complex logistics distribution center location problem， and it also has outstanding practical performance.

Key words:

particle swarm optimization, optimization, fitness, inertia weight, mutation