A Species-Based Multi-Objective Genetic Algorithm for Multi-Objective Optimization Problems

被引：0
|
作者：
Sun Fuquan [1 ]
Wang Hongfeng [1 ]
Lu Fuqiang [1 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China
来源：
2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2014年
关键词：
multi-objective optimization problem; evolutionary multi-objective optimization; genetic algorithm; species;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In recent years, multi-objective optimization problems (MOOPs) have gained a lot of attentions from the community of evolutionary algorithm since many real-world optimization problems would involve multiple objective functions. In this paper, a species-based multi-objective genetic algorithm (SMOGA) that hybridizes a species method, which was initially designed in GA for multi-modal problems, with the algorithm mechanism of NSGA-II, which was one of well-known MOGAs, is proposed for MOOPs. In order to examine the performance of the proposed algorithm, experiments were carried out to investigate the strength and weakness of SMOGA on a series of test MOOPs in comparison with NSGA-II.
引用
收藏
页码：5063 / 5066
页数：4
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