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An adaptive multitask optimization algorithm based on competitive scoring
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DOI：10.1016/j.swevo.2024.101798.png)
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Abstract
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Evolutionary multitask optimization (EMTO) generally relies on transferring knowledge among different tasks to improve performance. However, how to reduce the impact of negative transfer in EMTO still remains a challenge, especially for complex many-task optimization problems. To address this issue, this article proposes an adaptive multitask optimization algorithm by introducing a competitive scoring mechanism (MTCS). Based on this mechanism, MTCS quantifies the effects of transfer evolution and self-evolution, and then adaptively sets the probability of knowledge transfer and selects source tasks. Then, a dislocation transfer strategy is designed in knowledge transfer to maximize the effects of evolution. MTCS is compared with ten state-of-the-art EMTO algorithms on multitask and many-task benchmark problems. The experimental results demonstrate the effectiveness of the proposed knowledge transfer strategy and the superiority of overall performance of MTCS.
Keywords:
Evolutionary algorithm
Multitask optimization
Knowledge transfer
Competitive scoring mechanism
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