A Multipopulation-Based Multiobjective Evolutionary Algorithm

Ma, H. and Fei, M. and Jiang, Z. and Li, L. and Zhou, H. and Crookes, D. (2018) A Multipopulation-Based Multiobjective Evolutionary Algorithm. IEEE Transactions on Cybernetics, . pp. 1-14. ISSN 2168-2267. (doi:https://doi.org/10.1109/TCYB.2018.2871473) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.

Item Type: Article
Uncontrolled keywords: Sociology;Statistics;Optimization;Mathematical model;Genetic algorithms;Evolutionary computation;Markov processes;Evolutionary algorithm;genetic algorithms (GAs);Markov chain;multiobjective optimization;multipopulation
Subjects: Q Science > Q Science (General)
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 23 Oct 2018 18:11 UTC
Last Modified: 19 Nov 2018 15:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69758 (The current URI for this page, for reference purposes)
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