背包问题求解的建模及性能分析  

Modeling and Performance Analysis of Knapsack Problem Solving

作　　者：蔡敏[1] 

机构地区：[1]青岛黄海学院基础教学部,山东青岛266427

出　　处：《内蒙古师范大学学报（自然科学汉文版）》2016年第1期13-16,共4页Journal of Inner Mongolia Normal University(Natural Science Edition)

基　　金：山东省自然科学基金资助项目(2014ZRB019MQ)

摘　　要：为了提高背包问题求解的性能,针对标准粒子群优化算法局部搜索能力差、收敛速度慢等缺陷,引入粒子速度权重值自适应调整策略对背包问题求解进行建模,并采用2个具体的背包问题解测试了改进粒子群优化(IPSO)算法的性能.测试结果表明,相对于对比算法,IPSO算法具有更优寻优能力和收敛速度,不仅能获得更高精度的背包问题求解,而且提高了背包问题求解的稳定性,尤其对于大规划的背包问题,优势十分显著.In order to improve the performance of knapsack problem and solve local search ability and convergence speed slow defects in standard particle swarm optimization algorithm,article velocity weighting value adaptive adjustment strategy is introduced to solve modeling of knapsack problem,the performance of the improved particle swarm optimization（IPSO）algorithm is tested by using two specific knapsack problems,compared with the other algorithms,IPSO algorithm has better convergence speed and searching,not only can obtain higher accuracy,but also improve the stability for knapsack problem,advantage is very obvious for large knapsack problem.

关 键 词：背包问题 求解方法 自适应调整 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]