在建立供应链优化模型和分析基本粒子群优化算法的基础上,提出了一种求解供应链优化问题的改进粒子群算法。在优化过程中,该算法以优良适应值粒子取代部分不良适应值粒子,使算法具有过滤能力,加快了搜索速度,并保证了收敛于全局最优解。实验结果与基本粒子群算法进行了验证和比较,表明该改进粒子群算法具有较好的性能和简单快速准确等特点。 On the basis of establishing the supply chain optimization model and analyzing the basic particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed to solve the supply chain optimization problem. In the process of optimization, the algorithm replaces some bad fitness particles with good fitness particles, which makes the algorithm have the ability to filter, speed up the search and ensure convergence to the global optimal solution. The experimental results and the basic PSO are validated and compared, which shows that the improved PSO has better performance and is simple, fast and accurate.