NDLI: Improving the optimization performance of NSGA-II algorithm by experiment design methods

Content Provider	IEEE Xplore Digital Library
Author	Shidong Qiao Xiang Dai Zhong Liu Jincai Huang Guangying Zhu
Copyright Year	2012
Description	Author affiliation: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China (Shidong Qiao; Xiang Dai; Zhong Liu; Jincai Huang; Guangying Zhu)
Abstract	NSGA-II is an effective multi-objective optimization algorithm, and how to further improve its optimizing performance is an interesting but difficult problem. The Orthogonal Array method(OA) and the Taguchi method are two important kinds of experiment design methods. In this paper, the classical genetic operators are replaced by these experiment design methods to generate new individuals in NSGA-II. This results into two hybrid NSGA-II algorithms, whose optimizing ability is approved by the experiments on the typical multi-objective test functions, and the algorithm combined with Taguchi method is better than the other one with OA, while the calculation complexity of the former is a little higher. In fact, the differences between NSGA-II and the two hybrid algorithms are just the steps to generate new individuals, and the hybrid algorithms don't change any other operations of NSGA-II, which makes them easy for implementation.
Starting Page	82
Ending Page	85
File Size	698585
Page Count	4
File Format	PDF
ISBN	9781457717789
ISSN	21591547
e-ISBN	9781457717796
DOI	10.1109/CIMSA.2012.6269589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Indexes Algorithm design and analysis Vectors Design methodology Arrays Complexity theory Non-dominated Sorting Genetic Algorithm II (NSGA-II) Orthogonal Array Method (OA) Taguchi Method multi-objective optimization (MO)
Content Type	Text
Resource Type	Article

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