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 multiobjective 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 mincost and max-flow schemes by iterative algorithm. Mine ventilation network was taken as example to conduct the test. The results show that the bi-objective genetic algorithm of network min-cost and maxflow is feasible and effective. The variable number is reduced in this algorithm and algorithm efficiency is improved.
作者简介: 厍向阳(1968—),男,陕西周至人,博士,副教授(xiangyangshe@sohu.com),主要从事数据挖掘与智能信息处理、人工智能与模式识别、复杂系统建模与优化等研究.Biobjective optimization o
引用本文:
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.
SHE Xiangyang. Bi-objective optimization of network min-cost and max-flow based on genetic algorithm[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2011, 32(3): 341-345.
2011, Vol. 32 
