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Dynamic Multivariation Multifactorial Evolutionary Algorithm for Large-Scale Multiobjective Optimization
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AI
DOI：10.1109/TETCI.2025.3597299.png)
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Abstract
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Problem transformation-based multiobjective evolutionary algorithms (MOEAs) face the risk of losing optimal solutions when transforming a large-scale multiobjective optimization problem into a low-dimensional variant. One possible solution is to conduct concurrent multivariation searches, where the original space is treated as the original task, and the transformed spaces are treated as auxiliary tasks. However, determining reasonable dimensions for these transformed spaces remains a challenge. To address this, we propose a novel multivariation multifactorial evolutionary algorithm that dynamically adjusts the auxiliary tasks, with each corresponding to a transformed space with an optimal dimension for the current evolutionary stage. The approach begins by initializing a pool of candidate tasks with varying dimensions, allowing them to participate in the evolutionary process. It then quantifies each candidate's contribution and selects the most efficient ones as auxiliary tasks at each evolutionary stage. Additionally, we propose a novel knowledge transfer strategy that enables accurate cross-task and cross-dimensional transfer by integrating high-dimensional and low-dimensional information, thereby enhancing information sharing efficiency among multiple optimization tasks. Experimental results from a benchmark test suite and various real-world problems indicate the superior performance of the proposed algorithm in the majority of cases, demonstrating strong competitiveness when compared to state-of-the-art MOEAs.
Keywords:
Optimization
Convergence
Search problems
Heuristic algorithms
Knowledge transfer
Accuracy
Transforms
Training
Particle swarm optimization
Information sharing
Large-scale multiobjective optimization
multiobjective evolutionary algorithms
multifactorial optimization
dimension of auxiliary task
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1.4K
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4.5K
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