[1]赵礼峰,纪亚宝. 最大流最小截问题的遗传算法研究[J].计算机技术与发展,2017,27(04):69-72.
　ZHAO Li-feng,JI Ya-bao. Investigation on Genetic Algorithm for Maximum FlowMinimum Cut Problem[J].,2017,27(04):69-72.
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 最大流最小截问题的遗传算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年04期
页码:
69-72
栏目:
智能、算法、系统工程
出版日期:
2017-04-10

文章信息/Info

Title:
 Investigation on Genetic Algorithm for Maximum FlowMinimum Cut Problem
文章编号:
1673-629X（2017）04-0069-04
作者:
 赵礼峰; 纪亚宝
 南京邮电大学 理学院
Author(s):
 ZHAO Li-feng; JI Ya-bao
关键词:
 ; 最大流最小截; 遗传算法; 选择; 交叉; 变异
Keywords:
 ; maximum flow minimum cut; genetic algorithm; selection; crossing; mutation
分类号:
TP301.6
文献标志码:
A
摘要:
 遗传算法在众多领域中均有重要应用,运用遗传算法同样可以求解最大流最小截问题.遗传算法解决最大流最小截问题可以有效地解决对于网络规模增长,传统算法计算量呈指数级增长的局限性.根据最大流最小截问题的相关理论和遗传算法的原理,设计出最大流最小截问题的遗传算法,根据最大流最小截问题的定义设计了遗传算法中的编码方法、解码方法以及群体初始化方法,形成算法的初始个体.设计适应度函数计算个体适应度,根据个体适应度设计算法的选择算子选择个体,设计了交叉算子和变异算子,将选择的个体进行交叉变异产生新的个体,并且设计了具体的算法步骤.通过仿真实验发现,对于小型网络和大型网络,该算法均能稳定求解,并且随着算法迭代次数的增加,算法求得最优解就越接近于真实解.
Abstract:
 Genetic algorithm has important applications in many fields,so the problems of maximum flow minimum cut also can be solved by it.Genetic algorithm solving the maximum flow minimum cut problem can be a solution for the exponential growth limitations of calculating amount for traditional algorithms with the increasing of the network size.Based on theory of maximum flow minimum cut problem and genetic algorithm principle,a genetic algorithm is designed for maximum flow minimum cut problem.According to the definition of maximum flow minimum cut,the encoding method,decoding method and a group initialization method of genetic algorithm are designed to form the initial individual of algorithm.The fitness function is designed to calculate individual fitness by which the selection operator is designed to select individual,and design of crossover and mutation operator,the selected individuals are carried out crossover and mutation to produce new individuals,introducing specific algorithm steps.Simulation results show that for small and large networks,this algorithm is stable,and with increasing number of iterations,it can obtain the optimal solution much closer to the true solution.

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更新日期/Last Update: 2017-06-16