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旅行商问题最优路径的改进免疫遗传算法
引用本文:孔令夷.旅行商问题最优路径的改进免疫遗传算法[J].数学杂志,2015,35(2):361-367.
作者姓名:孔令夷
作者单位:西安邮电大学经济与管理学院
基金项目:国家自然科学基金资助(71102149)
摘    要:本文研究了一种改进的求解旅行商问题最优路径的免疫遗传算法.结合随机法与贪心法生成初始种群,利用亲和度排序而选取抗体以得到复制群体,引入轮盘赌及克隆选择获取高亲和度抗体,并实施疫苗接种及免疫记忆更新抗体.运用免疫记忆机理的闭环逻辑,证明了该算法生成的城市序列是全局收敛的.数值实验证明该算法是有效的.

关 键 词:旅行商问题  免疫遗传算法  克隆选择  疫苗接种
收稿时间:2012/9/29 0:00:00
修稿时间:2014/4/20 0:00:00

IMPROVED IMMUNE GENETIC ALGORITHM FOR OPTIMAL PATH OF TRAVELLING SALESMAN PROBLEMS
KONG Ling-yi.IMPROVED IMMUNE GENETIC ALGORITHM FOR OPTIMAL PATH OF TRAVELLING SALESMAN PROBLEMS[J].Journal of Mathematics,2015,35(2):361-367.
Authors:KONG Ling-yi
Institution:KONG Ling-yi;School of Economics and Management,Xi’an University of Post and Telecommunications;
Abstract:In this paper, an improved immune genetic algorithm for solving optimal path of travelling salesman problem is introduced. Stochastic method and greedy method are combined to produce the initial chromosomes populations. Based on affiity sorting, antibodies are selected to gain replicated subguoup. Roulette and clone selection are introduced in sequence to get high affiity antibodies. Nextly, vaccination and immune memory operations are implemented to revamp antibody. Using immune memory mechanism, city array generated by the new algorithm is proved to be globally convergent. Numerical experiments prove that the new algorithm is valid.
Keywords:travelling salesman problem  immune genetic algorithm  clone selection  vaccination
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