NDLI: A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design

Content Provider	IEEE Xplore Digital Library
Author	Yuping Wang Chuangyin Dang Hecheng Li Lixia Han Jingxuan Wei
Copyright Year	2009
Description	Author affiliation: School of Computer Science and Technology, Xidian University, Xi'an, China (Yuping Wang; Hecheng Li; Lixia Han; Jingxuan Wei) \|\| Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, China (Chuangyin Dang)
Abstract	Designing efficient algorithms for difficult multi-objective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolutionary algorithm based on orthogonal and uniform design is proposed. First, the orthogonal design is used to generate initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to locate good points for further exploration in subsequent iterations. Second, to explore the search space efficiently and get uniformly distributed and widely spread solutions in objective space, a new crossover operator is designed. Its exploration focus is mainly put on the sparse part and the boundary part of the obtained non-dominated solutions in objective space. Third, to get desired number of well distributed solutions in objective space, a new clustering method is proposed to select the non-dominated solutions. Finally, experiments on thirteen very difficult benchmark problems were made, and the results indicate the proposed algorithm is efficient.
Starting Page	2927
Ending Page	2933
File Size	2033490
Page Count	7
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983311
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Algorithm design and analysis Scattering Clustering algorithms Design methodology Clustering methods Hypercubes Space exploration Computer science Design for experiments
Content Type	Text
Resource Type	Article

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