IMODE: Improving Multi-Objective Differential Evolution Algorithm

被引：7
|
作者：
Ji, Shan-Fan [1 ]
Sheng, Wu-Xiong
Jing, Zhuo-Wang
Cheng, Long-Gong
机构：
[1] HuaiHai Inst Technol, Sch Elect Engn, LianYunGang 222005, Peoples R China
来源：
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 1, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ICNC.2008.97
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Differential Evolutionary (DE) is an evolutionary algorithm that was developed to handle optimization problems. DE is a simple algorithm, but it has been successfully applied to selected real world multi-objective problems. In this paper, Improving Multi-objective Differential Evolutionary (IMODE) is a new approach to solve multi-objective optimization based on basic DE. This algorithm is equipped with contour line to select candidate individuals, and combines with the crowding distance sorting and Pareto-based ranking, and e dominance. The solutions provided by the IMODE algorithm for five standard test problems, is competitive to three known multi-objective optimization algorithms.
引用
收藏
页码：212 / +
页数：2
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