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• W2890583810 abstract "In multiobjective optimization, defining a good solution is a multifactored process. Most existing evolutionary multi- or many-objective optimization (EMO) algorithms have utilized two factors: 1) domination and 2) crowding levels of each solution. Although these two coarse-grained factors are found to be adequate in many EMO algorithms, their relative importance in an algorithm has been a matter of great concern to many current studies. We argue that beside these issues, other more fine-grained factors are of importance. For example, since extreme objective-wise solutions are important in establishing a noise-free and stable normalization process, reaching extreme solutions is more crucial than finding other solutions. In this paper, we propose an integrated algorithm, B-NSGA-III, that produces much better convergence and diversity preservation. For this purpose, in addition to emphasizing extreme objective-wise solutions, B-NSGA-III tries to find solutions near intermediate undiscovered regions of the front. B-NSGA-III addresses critical algorithmic issues of convergence and diversity-preservation directly through recent progresses in literature and integrates all these critical fine-grained factors seamlessly in an alternating phases scheme. The proposed algorithm is shown to perform better than a number of commonly used existing methods." @default.
• W2890583810 created "2018-09-27" @default.
• W2890583810 creator A5030501191 @default.
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• W2890583810 date "2019-06-01" @default.
• W2890583810 modified "2023-10-03" @default.
• W2890583810 title "Multiphase Balance of Diversity and Convergence in Multiobjective Optimization" @default.
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• W2890583810 doi "https://doi.org/10.1109/tevc.2018.2871362" @default.
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