基于多子种群和密度估计的高维多目标进化算法的冷轧负荷分配

doi:10.3969/j.issn.1000-1158.2022.01.10

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Abstract A many-objective evolutionary algorithm based on multi-subpopulation and density estimation is proposed. The sub-populations are divided by reference vectors, and the degree of approaching the Pareto front is improved by the convergence mechanism. In addition, the individual density is evaluated by calculating the euclidean distance between individual and reference vector, in order to maintaining the diversity of the solution set. The results of numerical simulation indicate that the convergence and diversity of the solution set obtained by the proposed algorithm is obviously better than the comparison algorithms. Finally, the load distribution is optimized by the proposed algorithm for many-objective optimization. The optimized load distribution scheme can reduce energy consumption by 2.2%, and improves product quality.

Key words： metrology load distribution of cold rolling sub-populations density estimation many-objective

Received: 18 January 2021 Published: 06 January 2022

PACS:	TB973
	TB931

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	Articles by authors
	ZHAO Zhi-wei
	LIU Yue
	XIONG Zhi-jian
	YANG Xiu-wei

Cite this article:

ZHAO Zhi-wei,LIU Yue,XIONG Zhi-jian, et al. Many-objective Evolutionary Algorithm Based on Multi-subpopulation and Density Estimation for Load Distribution of Cold Rolling[J]. Acta Metrologica Sinica, 2022, 43(1): 65-71.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.01.10 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I1/65

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