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An adaptive multitask optimization algorithm based on competitive scoring
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AI
DOI：10.1016/j.swevo.2024.101798.png)
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Abstract
En 中文
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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