改进的蛙跳算法在多目标优化问题中的应用  

Application of improved Shuffle Frog Leaping Algorithm to resolve multi-objective optimization problem

作　　者：王晓笛[1] 肖伟[1] 何灿 

机构地区：[1]湖南师范大学,长沙410081 [2]湖南星辰在线网络传播有限公司,长沙410016

出　　处：《计算机工程与应用》2012年第30期233-238,共6页Computer Engineering and Applications

基　　金：湖南省教育厅资助科研项目(No.09C648)

摘　　要：在介绍原始混洗蛙跳算法的基础上,引入遗传算法中的遗传算子,改进原始蛙跳算法的分组方法,提出一种改进的混洗蛙跳算法用于求解多目标优化问题。改进的算法以多目标0-1背包问题为例进行模拟实验,其实验结果表示,与原始的混洗蛙跳算法相比较,改进的蛙跳算法在求解多目标优化问题上具有更好的性能。This paper draws genetic operators of GA and improves the method of SFLA group dividing based on introducing SFLA, puts forward an improved SFLA to resolve problem of multi-objective optimization. The improved method takes multi-objective 0-1 knapsack as an example for simulated experiment, which bears out that, compared with original SFLA, the improved SFLA has better performance on resolving improved SFLA problem.

关 键 词：混洗蛙跳算法 多目标优化问题 遗传算子 分组方法 多目标0-1背包问题 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]