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A Knowledge Guided Transfer Strategy for Evolutionary Dynamic Multiobjective Optimization
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DOI：10.1109/TEVC.2022.3222844.png)
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摘要
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The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy (KTS)-based DMOEA is proposed in this article. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with the knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the KTS outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.
Keyword:
Statistics
Sociology
Optimization
Heuristic algorithms
Transfer learning
Evolutionary computation
Manifolds
Dynamic multiobjective optimization
knowledge transfer
random change
regular change
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12
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1.9K
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2.4W
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