WBMOIGA: Weight-Based Multiobjective Immune Genetic Algorithm and Its Application
In this paper, we present a weight-based multiobjective immune genetic algorithm (WBMOIGA). Compared to other weight-based multiobjective genetic algorithms, the proposed algorithm shows the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function, and (2) a local search procedure is utilized to improve the quality of the population, and an immune operator is adopted to increase the diversity of the population, and (3) specifically, a new mate selection operator, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection operator, named truncation algorithm with similar individuals (TASI), are presented. Simulation results on six standard test problems (ZDT1,ZDT2,ZDT3,ZDT6,SCH2,and FON) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist nondominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA). Moreover, when applied to parallel machine scheduling, WBMOIGA also demonstrates better performance than NSGA-II and RWGA.
Jiaquan Gao
Zhijiang College,Zhejiang University of Technology,Hangzhou,Zhejiang,310024
国际会议
2009 IEEE International Conference on Information and Automation(2009年 IEEE信息与自动化国际学术会议)
珠海、澳门
英文
1-6
2009-06-22（万方平台首次上网日期，不代表论文的发表时间）