基于微分演化的PSO参数选择策略

粒子群优化方法（Particle Swarm Optimization，PSO）是由Kennedy和Eberhart于1995年提出的一种基于群体智能（Swarm Intelligence）的演化计算技术，用于求解各类优化问题。PSO方法通过各种参数控制粒子的运行轨迹，并对参数设置有很强的敏感性。因此，如何为PSO方法选择最优的参数是PSO方法的关键。本文提出了一种不依赖个人经验的参数选则策略，针对特定问题，将PSO方法的性能表示成参数的函数，从而将参数选择问题转变成函数优化问题。采用微分演化（Differential Evolution，DE）方法对该函数进行优化，来确定PSO的最佳参数，收到了较好的效果。

The Strategy of Parameters-selection for PSO Based on Differential Evolutions

The Particle Swarm Optimization (PSO)method was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully in various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. The track of each particle is controlled by some parameters and highly sensitive to different parameters setting. So how to choice the optimum parameters is key for PSO. The strategy of parameter-selection is proposed, which dose not depend on expert experience, the performance of PSO is be regarded as a function decided by parameters of PSO in this strategy, So, the problem of parameters-selection is be transform to optimization problem, at the same time, differential evolution (DE)is be used for solving this optimization problem.