Species-based genetic algorithm for multiobjective optimization problems

被引：0
|
作者：
Wang, Hong-Feng [1 ]
Zhang, Qian [1 ]
Li, Xiao-Jiang [2 ,3 ]
机构：
[1] School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
[2] The Second Research Institute of CAAC, Chengdu 610041, China
[3] Civil Aviation Electronic Engineering Design Institute, Chengdu 610041, China
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2014年 / 35卷 / 04期
关键词：
Multiobjective optimization - Pareto principle - Benchmarking;
D O I：
10.3969/j.issn.1005-3026.2014.04.006
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Considering that evolutionary multiobjective optimization has been becoming one of research topics in evolutionary algorithm community recently, a species-based mechanism was introduced into multi-objective GA, which is initially applied into GA for multi-modal optimization problem. And then a new species-based method was designed and proposed for multiobjective optimization problem, which comprises a species seed indentifying strategy based on Pareto optimality and crowd degree and an adaptive species constructing scheme. Experimental results showed that the proposed species-based multiobjective GA outperforms nondominated sorting GA II(NSGA-II) on a set of benchmark test problems.
引用
收藏
页码：479 / 483
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