改进PSO算法在二维下料问题中的研究  

Research of Improved PSO Algorithms on Two-Dimensional Cutting-Stock Problem

作　　者：张菡[1] 

机构地区：[1]菏泽学院计算机与信息工程系,山东菏泽274000

出　　处：《电脑与电信》2014年第1期50-52,共3页Computer & Telecommunication

摘　　要：粒子群优化(PSO)算法是一种基于集群智能的进化计算方法,在该方法中粒子通过追随自己找到的最优解和种群最优解完成优化。文章将PSO算法应用到三角形优化下料问题的研究中,给出了具体的实施流程,为了提高PSO算法的收敛精度,避免早熟现象的产生,对PSO进行了改进,提出一种启发式PSO算法。通过对三角形的优化下料进行仿真,仿真结果显示改进后的启发式粒子群优化算法在收敛效果和材料的利用率方面均有显著的提高。PSO is an evolution algorithm based on cluster. In the method, particles are optimized by the optimal solution they find and the population optimal solution. This paper applies the PSO algorithm to the triangle optimization cutting stock problem. In order to improve the convergence precision of PSO algorithm and to avoid precocious phenomena, this paper puts forward a heuris-tic Particle Swarm Optimization. The experimental results show that the proposed algorithm has improved significantly in conver-gence effect and the utilization rate of materials.

关 键 词：二维下料 启发式搜索算法 粒子群优化算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]