NDLI: Simultaneous use of different scalarizing functions in MOEA/D

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Simultaneous use of different scalarizing functions in MOEA/D

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Simultaneous use of different scalarizing functions in MOEA/D

Content Provider	ACM Digital Library
Author	Nojima, Yusuke Ishibuchi, Hisao Sakane, Yuji Tsukamoto, Noritaka
Abstract	The use of Pareto dominance for fitness evaluation has been the mainstream in evolutionary multiobjective optimization for the last two decades. Recently, it has been pointed out in some studies that Pareto dominance-based algorithms do not always work well on multiobjective problems with many objectives. Scalarizing function-based fitness evaluation is a promising alternative to Pareto dominance especially for the case of many objectives. A representative scalarizing function-based algorithm is MOEA/D (multiobjective evolutionary algorithm based on decomposition) of Zhang & Li (2007). Its high search ability has already been shown for various problems. One important implementation issue of MOEA/D is a choice of a scalarizing function because its search ability strongly depends on this choice. It is, however, not easy to choose an appropriate scalarizing function for each multiobjective problem. In this paper, we propose an idea of using different types of scalarizing functions simultaneously. For example, both the weighted Tchebycheff (Chebyshev) and the weighted sum are used for fitness evaluation. We examine two methods for implementing our idea. One is to use multiple grids of weight vectors and the other is to assign a different scalarizing function alternately to each weight vector in a single grid.
Starting Page	519
Ending Page	526
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830577
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Evolutionary multiobjective optimization (emo) Moea/d Scalarizing function
Content Type	Text
Resource Type	Article

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