NDLI: A Multi-Objective Evolutionary Algorithm based on complete-linkage clustering to enhance the solution space diversity

Content Provider	IEEE Xplore Digital Library
Author	Tahernezhad, K. Lari, K.B. Hamzeh, A. Hashemi, S.
Copyright Year	2012
Description	Author affiliation: CSE and IT Department, Shiraz University, Iran (Tahernezhad, K.; Lari, K.B.; Hamzeh, A.; Hashemi, S.)
Abstract	Multi-Objective Evolutionary Algorithm (MOEA) is a leader framework to solve multi-objective optimization problems due to its capability of obtaining a set of compromise solutions in a single run. Most of MOEAs try to converge to the Pareto optimal front in purpose of maintaining the population diversity in the objective space. Here, we are going to present a novel MOEA for enhancing the population diversity of non-dominated vectors in the solution space. In this paper, a novel approach, which is inspired from geometrical information of candidate solutions, is proposed to adopt the innovative clustering-based scheme during the optimization cycle. This approach intends to obtain more diverse and well-distributed non-dominated vectors (i.e. Pareto-set) in the solution space. The present work is applied to a wide range of well established test problems. The obtained results validate the motivation on the basis of diversity and performance measures in comparison to state of the art algorithms.
Starting Page	128
Ending Page	133
File Size	829670
Page Count	6
File Format	PDF
ISBN	9781467314787
e-ISBN	9781467314794
DOI	10.1109/AISP.2012.6313731
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Couplings Pareto-Optimal set Diversity indicator Sociology Evolutionary computation Diversity methods Vectors Multi-objective optimization Hierarchical clustering Dendrogram Statistics Optimization
Content Type	Text
Resource Type	Article

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