Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and...

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Vydáno v:Computer modeling in engineering & sciences Ročník 140; číslo 1; s. 363 - 383
Hlavní autoři: Chen, Liang, Zhang, Jingbo, Wu, Linjie, Cai, Xingjuan, Xu, Yubin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2024
Témata:
Algorithms
Convergence
Multiple objective analysis
Optimization
Optimization algorithms
Subgroups
Variables
ISSN:1526-1506, 1526-1492, 1526-1506
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Shrnutí:The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set threshold, that group undergoes a process of weighting and overlapping grouping. Specifically, the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables. The decision variable with the highest interaction in the group is identified and disregarded, and the remaining variables are then reclassified into subgroups. Finally, the decision variable with the strongest interaction is added to each subgroup. MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups, which contributed to the optimized direction of convergence and diversity exploration with different groups. MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems, and the experimental results demonstrate the effectiveness of our methods. Compared with the other algorithms, our method is still at an advantage.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2024.049044