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• W1564090841 abstract "Achieving balance between convergence and diversity is a basic issue in evolutionary multiobjective optimization (EMO). In this paper, we propose a hybrid EMO algorithm that assigns different selection principles to two separate and co-evolving archives. Particularly, one archive maintains a repository with a competitive selection pressure towards the Pareto-optimal front (PF), the other preserves a population with a satisfied distribution in the objective space. Furthermore, to exploit guidance information towards the Pareto-optimal set (PS), we develop a restricted mating selection mechanism to select mating parents from each archive for offspring generation. Empirical studies are conducted on a set of benchmark problems with complicated PSs. Experimental results demonstrate the effectiveness and competitiveness of our proposed algorithm in balancing convergence and diversity." @default.
• W1564090841 created "2016-06-24" @default.
• W1564090841 creator A5000546219 @default.
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• W1564090841 date "2015-05-01" @default.
• W1564090841 modified "2023-09-27" @default.
• W1564090841 title "Evolutionary multiobjective optimization with hybrid selection principles" @default.
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• W1564090841 doi "https://doi.org/10.1109/cec.2015.7256986" @default.
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