摘要
针对多背包问题最优解的求解,设计了一种新的价值密度;在此基础上结合传统的贪心算法,提出了一种求解多背包问题的混合遗传算法。该算法采用整数编码,并采用轮盘赌选择方法,对背包资源利用不足的可行解进行修正处理,对不可行解进行修复处理。并在大量的数值实验的基础上,将该方法与传统方法及简单遗传算法进行比较,实验结果表明,该混合遗传算法提高了问题求解的速度和精度,有一定的优越性。
This paper designs a new profit-density for solving multi-knapsack problem firstly,and then proposes a new Hybrid Genetic Algorithm（HGA） based on greedy algorithm.The algorithm uses the integer code,applies roulette wheel selection method,amends the feasible solution which knapsack resources are insufficient for use,and repairs the infeasible solution.Finally this paper compares HGA with other common mathematical methods and Simple Genetic Algorithm（SGA） for solving this problem on the basis of many numerical experiments,the results show that HGA is more efficient than other methods in the speed and accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第20期45-48,共4页
Computer Engineering and Applications
基金
中国科学院知识创新工程重要方向项目(No.KZCX2-yw-203-2)
关键词
多背包问题
不可行解
贪心法
遗传算法
multi-knapsack problem
infeasible solution
greedy algorithm
genetic algorithm
