江苏大学学报（自然科学版）2011，Vol.32Issue(3)：341-345,358,6.DOI:10.3969/j.issn.1671-7775.2011.03.019
网络最小费用最大流双目标遗传优化算法
Bi-objective optimization of network min-cost and max-flow based on genetic algorithm
摘要
Abstract
Aimed at the defect of transfering network min-cost and max-flow to single objective optimization, the bi-objective optimization model of network min-cost and max-flow was proposed, and multi-objective genetic algorithm was adopted. The flow values of remain branches were encoded and initialized by multi-objective genetic algorithm, and the flow values of tree branches were calculated by decoding and circuit matrix. Based on network min-cost and max-flow function, nodes capacity and branches capacity restrictions, the generalized bi-objective function were set up according to multi-objective optimization theory. The flow scheme codes were evaluated by the generalized bi-objective function and evolved by evolution arithmetic operators to obtain optimization min-cost and max-flow schemes by iterative algorithm. Mine ventilation network was taken as example to conduct the test. The results show that the biobjective genetic algorithm of network min-cost and max-flow is feasible and effective. The variable number is reduced in this algorithm and algorithm efficiency is improved.关键词
网络/网络最小费用最大流/最小支撑树/多目标优化/遗传算法Key words
network/ network min-cost and max-flow/ minimum spanning trees/ multi-objective optimization/ genetic algorithm分类
信息技术与安全科学引用本文复制引用
厍向阳..网络最小费用最大流双目标遗传优化算法[J].江苏大学学报（自然科学版）,2011,32(3):341-345,358,6.基金项目
陕西省自然科学基金资助项目(2009JM7007) （2009JM7007）
陕西省教育厅专项科研计划项目(08JK354) （08JK354）
