粒子群优化算法的性能分析和参数选择

惯性权重和加速因子是影响粒子群算法优化性能的重要参数.基于常用的12个测试函数，本文通过实验研究了不同参数组合下粒子的探索能力和算法的优化性能，在此基础上推荐了一组固定的参数组合.通过惯性权重和加速因子的不同变化策略组合对算法性能影响的实验分析，推荐了一种变化的参数设置方法.基于CEC2015发布的15个基准函数进一步验证了本文推荐的参数选取方法的有效性.最后讨论了粒子群优化（Particle swarm optimization，PSO）算法在连续优化和离散优化方面的应用问题.

Performance Analysis and Parameter Selection of PSO Algorithms

Inertia weight and acceleration factors have significant impact on the performance of particle swarm optimization (PSO) algorithm. Through simulation experiments on twelve classical benchmark functions, this paper studies the algorithm's exploitation ability and optimization performance with different parameters. Based on the experimental results, we recommend a setting for fixed parameters. Furthermore, we study the situation where inertia weight remains unchanged and acceleration factors change with iterations. Then a setting for varying parameters is recommended. The recommended parameters setting methods are verified through 15 benchmark functions that were published in CEC2015. At the end of the paper, a discussion of the PSO application issue on continuous optimization problems and discrete optimization problems is given.