摘要
根据柔性车间调度问题提出基于解空间距离聚类和变邻域搜索的粒子群算法.在粒子群算法基础上采用贪婪策略引入变邻域搜索方式,即调整关键路径上最大关键工序的机器位置,调整关键路径上工序相对位置变化,加强局部搜索能力;根据机器加工工序的空间距离,采用K-means聚类得到机器加工工序"优良个体",加大局部搜索性能.同时对于粒子群算法速度更新采用局部停滞策略,保留局部片段相对位置不变特性.通过实验仿真,优化算法取得了较好的效果,与一般的粒子群算法相比较收敛速度迅速且性能良好.
Based on the Flexible Job-Shop Scheduling Problem（FJSP）, an improved particle swarm optimization algorithm is proposed, which is based on solution space distance clustering and variable neighborhood search. In this algorithm, a better solution to the problem is that the greedy strategy is adopted to introduce a variable neighborhood search method, adjusting machine location of the biggest key processes on the critical path, adjusting the relative position changes which is on the critical path. According to the space distance of the machining process, the K-means clustering is used to get the ＂excellent individuals＂ of machine processing, increasing the local search performance. At the same time, the speed of the particle swarm optimization is updated with the local self-adaptive stagnation strategy, and the relative position of the local segment could be kept unchanged. Through the experimental simulation, the optimization algorithm achieves good effectiveness, and the convergence speed is rapider and the performance is better compared with the general PSO algorithm.
出处
《计算机系统应用》
2016年第12期143-148,共6页
Computer Systems & Applications
关键词
柔性车间调度
变邻域搜索
空间距离K-means聚类
速度局部停滞
flexible job shop scheduling problem
variable neighborhood search
space distance K-means clustering
velocity local stagnation
