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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Many-Objective Optimization for Knapsack Problems Using Correlation-Based Weighted Sum Approach
verfasst von : Tadahiko Murata, Akinori Taki
Erschienen in: Evolutionary Multi-Criterion Optimization
Verlag: Springer Berlin Heidelberg
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In this paper, we examine the effectiveness of an EMO (Evolutionary Multi-criterion Optimization) algorithm using a correlation based weighted sum for many objective optimization problems. Recently many EMO algorithms are proposed for various multi-objective problems. However, it is known that the convergence performance to the Pareto-frontier becomes weak in approaches using archives for non-dominated solutions since the size of archives becomes large as the number of objectives becomes large. In this paper, we show the effectiveness of using a correlation information between objectives to construct groups of objectives. Our simulation results show that while an archive-based approach, such as NSGA-II, produces a set of non-dominated solutions with better objective values in each objective, the correlation-based weighted sum approach can produce better compromise solutions that has averagely better objective values in every objective.