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Dynamic Grouping With a Self-Aware Computational Resource Allocation for Large-Scale Multi-Objective Optimization
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AI
DOI：10.1109/TEVC.2025.3564335.png)
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Abstract
En 中文
In real-world applications, many problems are characterized by numerous variables and conflicting objectives, which are referred to as large-scale multiobjective optimization problems (LSMOPs). The cooperative coevolution (CC) algorithm is commonly used to solve LSMOPs. The decision variables of the target problem are partitioned into groups with each group being solved independently. However, existing multiobjective CC algorithms encounter difficulties in accurately grouping variables by importance and lack effective methods for allocating computational resources to enhance solution efficiency. To address these challenges, a dynamic grouping method based on variable importance is introduced alongside a self-aware computational resource allocation mechanism. Our proposed algorithm calculates the importance of each variable by considering the impact of value fluctuations on each objective. Variables with similar importance levels are subsequently grouped together. Based on this, the resource allocation mechanism is proposed to monitor changes in the improvement rate of population quality during the optimization process across different variable groups, dynamically directing resources to groups of greater importance. The proposed algorithm was tested against five state-of-the-art large-scale multiobjective algorithms (RG-DRA, LERD, LMOCSO, LMEA, and FDV) using the LSMOP and WFG benchmark suites. It outperformed at least 75% of LSMOP instances and surpassed RG-DRA, LERD, and LMOCSO in over 50% of WFG instances, demonstrating comparable performance to LMEA and FDV.
Keywords:
Cooperative coevolution (CC)
dynamic grouping
large-scale multiobjective optimization
resource allocation
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