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Multitasking optimization algorithm based on a multitransfer strategy
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AI
DOI：10.1007/s12293-025-00446-6.png)
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Abstract
En 中文
To transfer effective knowledge between tasks, improve the effectiveness of intertask interactions and enhance the performance of evolutionary multitasking optimization algorithms, this article presents an adaptive evolutionary multitasking optimization algorithm based on a multitransfer strategy (EMTO-MS). EMTO-MS uses two transfer strategies for intertask knowledge transfer and adaptively allocates computational resources to these transfer strategies based on the performance of each strategy during the evolutionary process. In addition, the strength of interaction between two tasks is adaptively adjusted according to the transfer success rate and the similarity between the two tasks during the optimization process. To evaluate the performance of EMTO-MS, we compared EMTO-MS with nine mainstream evolutionary multitasking algorithms on the single-objective multitasking benchmarks,and with five outstanding EMTO algorithms on a complex multitask benchmark. The experimental results demonstrate that the proposed EMTO-MS outperforms most of the compared algorithms in terms of search accuracy and convergence.
Keywords:
Multitasking optimization
knowledge transfer
transfer strategy
similarity
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2.3
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458
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