A dynamic multi-objective evolutionary algorithm based on prediction

The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems; the main reasons are the objective’s conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the...

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Vydáno v:Journal of computational design and engineering Ročník 10; číslo 1; s. 1 - 15
Hlavní autoři: Wu, Fei, Chen, Jiacheng, Wang, Wanliang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford University Press 01.02.2023
한국CDE학회
Témata:
기계공학
prediction
knee point
convergence
dynamic multi-objective optimization
diversity maintenance strategy
close-to-boundary point
ISSN:2288-5048, 2288-4300, 2288-5048
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Shrnutí:The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems; the main reasons are the objective’s conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the dynamic optimization issue. First, we introduce a decision variable clustering and screening strategy that clusters the decision space of the non-dominated solution set to find the cluster centroids and then employs a decision variable screening strategy to filter out solutions that have an impact on the distribution of individuals. This approach can broaden the range of dynamic multi-objective optimization algorithms. Second, an approach for predicting special points is suggested. The algorithm’s convergence is improved following environmental changes by forecasting the special point tracking Pareto front in the object space. Finally, the forward-looking center points are used to predict the non-dominated solution set and eliminate the useless individuals in the population. The prediction strategy can help the solution set converge while maintaining its diversity, which is compared with the four other state-of-the-art strategies. Our experimental results demonstrate that the proposed algorithm, DSSP, can effectively tackle DMOPs. Graphical Abstract Graphical Abstract
ISSN:2288-5048
2288-4300
2288-5048
DOI:10.1093/jcde/qwac124