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A Multi-population Based Multi-objective Evolutionary Algorithm

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journal contribution
posted on 08.03.2019, 11:01 by H Ma, M Fei, Z Jiang, L Li, H Zhou, D Crookes
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.

Funding

National Natural Science Foundation of China; Fund for China Scholarship Council; UK EPSRC; Royal Society Newton Advanced Fellowship;

History

Citation

IEEE Transactions on Cybernetics

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cybernetics

Publisher

Institute of Electrical and Electronics Engineers, in press

issn

2168-2267

eissn

2168-2275

Acceptance date

17/09/2018

Copyright date

2018

Available date

08/03/2019

Publisher version

https://ieeexplore.ieee.org/document/8482330/authors

Language

en

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