计算机工程与应用2011，Vol.47Issue(26)：43-45,65,4.DOI:10.3778/j.issn.1002-8331.2011.26.013
利用多目标量子粒子群算法求解背包问题
Multi-objective quantum particle swarm optimization based on game theory for knapsack problem
摘要
Abstract
This paper presents a multi-objective quantum Particle Swarm Optimization(PSO) based on game theory.The algorithm for each objective function will be seen as an agent,agent to control the populations of the direction of their most advantageous to search, and then participate in it as a game participant.With the existence of a sequence games of repeated game model,in repeated game,not every game has produced the maximum benefit,but to the overall maximum benefit.And the algorithm is solving multi-objective 0/1 knapsack problem.The simulation results show that this algorithm can be found near the Pareto optimal front of a better solution,while maintaining the uniformity of the distribution solution.关键词
量子粒子群/多目标优化/背包问题/博弈论Key words
Quantum Particle Swarm Optimization(QPSO)/multi-objective optimization/knapsack problem/game theory分类
信息技术与安全科学引用本文复制引用
刘金江,刘峰..利用多目标量子粒子群算法求解背包问题[J].计算机工程与应用,2011,47(26):43-45,65,4.基金项目
河南省科技厅科技攻关项目(No.092102110274). （No.092102110274）
