进化算法
水准点(测量)
数学优化
趋同(经济学)
选择(遗传算法)
计算机科学
人口
多目标优化
最优化问题
数学
人工智能
人口学
大地测量学
社会学
地理
经济
经济增长
作者
Shuzhi Gao,Leiyu Yang,Yimin Zhang
标识
DOI:10.1016/j.asoc.2023.110232
摘要
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.
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