Genetic algorithm for pareto optimum-based route selection

作     者：Cui Xunxue Li Qin Tao Qing 

作者机构：New Star Research Inst. of Applied Technology Hefei 230031 P. R. China Jiangsu Key Lab of Computer Information Processing Technology Soochow Univ. Suzhou 215006 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2007年第18卷第2期

页      面：360-368页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：the Natural Science Foundation of Anhui Province of China (050420212) the Excellent Youth Science and Technology Foundation of Anhui Province of China (04042069) 

主　　题：Route selection Multiobjective optimization Pareto optimum Multi-constrained path Genetic algorithm. 

摘      要：A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.