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Oct.  2023

IEEE/CAA Journal of Automatica Sinica

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K. J. Qiao, J. Liang, Z. Y. Liu, K. J. Yu, C. T. Yue, and  B. Y. Qu,  “Evolutionary multitasking with global and local auxiliary tasks for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1951–1964, Oct. 2023. doi: 10.1109/JAS.2023.123336
Citation: K. J. Qiao, J. Liang, Z. Y. Liu, K. J. Yu, C. T. Yue, and  B. Y. Qu,  “Evolutionary multitasking with global and local auxiliary tasks for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1951–1964, Oct. 2023. doi: 10.1109/JAS.2023.123336

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

doi: 10.1109/JAS.2023.123336
Funds:  This work was supported in part by the National Natural Science Fund for Outstanding Young Scholars of China (61922072), the National Natural Science Foundation of China (62176238, 61806179, 61876169, 61976237), China Postdoctoral Science Foundation (2020M682347), the Training Program of Young Backbone Teachers in Colleges and Universities in Henan Province (2020GGJS006), and Henan Provincial Young Talents Lifting Project (2021HYTP007)
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  • Constrained multi-objective optimization problems (CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers. To solve CMOPs, constrained multi-objective evolutionary algorithms (CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking (EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front (PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

     

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    Highlights

    • A new evolutionary multitasking-based constrained multi-objective algorithm CMEGL is developed
    • A global auxiliary task and a local auxiliary task are created to assist the main task
    • A self-adaptive method is proposed to stop updating the population of global auxiliary task
    • CMEGL is tested on three different test sets

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