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Evolutionary multiobjective optimization with hybrid selection principles

Li, K and Deb, K and Zhang, Q (2015) Evolutionary multiobjective optimization with hybrid selection principles. In: UNSPECIFIED, ? - ?.

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 14 Dec 2016 09:27
Last Modified: 30 Mar 2021 15:15

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