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A non-revisiting framework for evolutionary multi-task optimization
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AI
DOI：10.1007/s10489-023-04918-5.png)
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Abstract
En 中文
Multi-task optimization is an emerging research topic in evolutionary computation, which aims to solve multiple optimization tasks simultaneously through knowledge transfer. However, existing multi-task evolutionary algorithms suffer from the re-evaluation problem, leading to unnecessary consumption of computing resources. To address this issue, a non-revisiting framework is proposed, which allows the non-revisiting scheme to be aided by historical information during the evolutionary search process. Moreover, an individual updating strategy is designed to improve the search efficiency of the algorithm and enhance the ability to escape local optima. Furthermore, a parallel scheme of the proposed framework is developedNational Frontiers Science Center for Industrial Intelligence and Systems Optimizationcomputation time on the CUDA architecture. To evaluate the effectiveness of the proposed framework, it is integrated with success-history based adaptive differential evolution. A comparative study of the proposed algorithm with eight state-of-the-art multi-task evolutionary algorithms is performed on nine benchmark problems. The experimental results demonstrate that the proposed algorithm outperforms the existing algorithms, highlighting its potential for solving multi-task optimization problems.
Keywords:
Multi-task optimization
Non-revisiting scheme
CUDA platform
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