基于自适应参数与混沌搜索的PSO算法求解柔性作业车间调度问题

针对传统粒子群优化(PSO)算法在求解柔性作业车间调度问题中的不足，提出了基于自适应参数与混沌搜索的粒子群优化算法。对粒子群算法中的惯性系数等参数采用基于迭代搜索而自适应调整的方式，使粒子在初期以较大惯性进行大范围搜索，后期逐渐减小惯性而转入精细搜索。这种方法改变了传统粒子群算法在求解过程中的盲目随机与求解精度不高的问题；同时，通过在局部搜索过程中引入混沌技术，扩大对最优解的寻找范围，以此避免算法陷入局部最优，有效提高算法的全局寻优能力。实验结果表明，基于自适应参数与混沌搜索的粒子群优化算法在求解柔性作业车间调度问题(FJSP)时能够获得更优粒子适应度平均值及更好的优化目标。所提算法对求解柔性作业车间调度问题可行，有效。

Particle swarm optimization algorithm with self-adaptive parameter and chaos search for solving flexible job shop scheduling problem

According to the shortcomings of traditional Particle Swarm Optimization(PSO) algorithm in solving flexible job-shop scheduling problem,this paper proposed particle swarm optimization algorithm with self-adaptive parameters and chaos search.According to the self-adaptive method of parameters such as inertia coefficient based on iteration,particles searched in large scale with high inertia at early time and then got into fine search by reducing inertia.This method solved the problem of blind randomness and low accuracy in traditional particle swarm algorithm.Introduction of chaos technology into local search could expand the search scale for optimization solution and reduce the possibility of falling into local extremum.This method could effectively improve global optimization ability of the algorithm.The experimental results show that the particle swarm optimization algorithm with self-adaptive parameters and chaos search can get more optimal average particle fitness and better optimization objective.The proposed algorithm is feasible and effective for Flexible Job-shop Scheduling Problem(FJSP).