Dynamic multi-objective evolutionary algorithm based on decomposition with hybrid prediction

The proposed dynamic multi-objective evolutionary algorithm, DMOEA/D-HP, addresses temporal variations in both the Pareto Front (PF) and Pareto Set (PS) for dynamic multi-objective optimization problems (DMOPs). Utilizing a hybrid prediction approach, the algorithm adapts to the dynamic nature of th...

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Vydáno v:Journal of physics. Conference series Ročník 2764; číslo 1; s. 12090 - 12096
Hlavní autoři: Zhao, Shenjia, Zhang, Hairui, Lyu, Rui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.05.2024
Témata:
Evolutionary algorithms
Multiple objective analysis
Pareto optimization
ISSN:1742-6588, 1742-6596
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Shrnutí:The proposed dynamic multi-objective evolutionary algorithm, DMOEA/D-HP, addresses temporal variations in both the Pareto Front (PF) and Pareto Set (PS) for dynamic multi-objective optimization problems (DMOPs). Utilizing a hybrid prediction approach, the algorithm adapts to the dynamic nature of the problem. The population is divided into three segments for prediction: individuals with a distance greater than a threshold in PS for central prediction, those with a distance less than a threshold in PS for differential evolutionary prediction, and the remaining individuals for cross-mutation to maintain diversity. To assess DMOEA/D-HP’s effectiveness, it is compared with three advanced algorithms in DMOP by using the DF test set. Experimental results demonstrate that DMOEA/D-HP outperforms in terms of distribution and convergence when solving DMOPs.
Bibliografie:ObjectType-Article-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2764/1/012090