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Volume 12 Issue 11
Nov.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(11): 2239-2252
Y. Xie, J. Qiao, D. Wang, and M. Yuan, “A novel self-adjusting dual-mode evolutionary framework for multi-task optimization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 11, pp. 2239–2252, Nov. 2025. doi: 10.1109/JAS.2025.125273
Citation: Y. Xie, J. Qiao, D. Wang, and M. Yuan, “A novel self-adjusting dual-mode evolutionary framework for multi-task optimization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 11, pp. 2239–2252, Nov. 2025. doi: 10.1109/JAS.2025.125273
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A Novel Self-Adjusting Dual-Mode Evolutionary Framework for Multi-Task Optimization

doi: 10.1109/JAS.2025.125273
Funds:  This work was supported in part by the Plan of Key Scientific Research Projects of Colleges and Universities in Henan Province (25A413005, 24A120005), the National Science and Technology Major Project (2021ZD0112302), and the National Natural Science Foundation of China (62222301, 61890930-5, 62021003)
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    Abstract
  • Evolutionary multi-task optimization (EMTO) presents an efficient way to solve multiple tasks simultaneously. However, difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases. Motivated by this, a novel self-adjusting dual-mode evolutionary framework, which integrates variable classification evolution and knowledge dynamic transfer strategies, is designed to compensate for this deficiency. First, a dual-mode evolutionary framework is designed to meet the needs of evolution in different states. Then, a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes. Second, a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes. Then, the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables. Third, an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge. Then, a dynamic weighting strategy is developed for efficient utilization of knowledge. Finally, by conducting experiments and comparing the designed method with several existing algorithms, the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances.

     

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