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A multiobjective evolutionary algorithm using multi-ecological environment selection strategy
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DOI：10.1016/j.asoc.2023.110232.png)
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Abstract
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For many-objective optimization problems (MaOPs), the conflict between convergence and diversity becomes more and more serious as the number of objectives increases. This paper proposes the evolutionary algorithm MeEA of multi-ecological environment selection strategy and uses this algorithm to solve MaOPs. Firstly, the objective space is divided into several different types of ecological environments. Secondly, the preference for convergence or diversity in the ecological environment is initially determined during environment selection and then the overall diversity maintenance of the population is ensured. Thirdly, the proposed algorithm is compared with five popular evolutionary algorithms on 44 multi-objective benchmark problems. Finally, it is applied to the optimization design of hydrodynamic lubrication radial sliding bearing of crane gearbox. Experimental results show that the performance of this algorithm is better than other algorithms in solving MaOPs.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Multi-objective optimization
Evolutionary computations
Multi-ecological environment
Convergence
Hydrodynamic sliding bearing
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6.6
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1.4W
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4.8W
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