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A Self-adaptive Single-Objective Multitasking Optimization Algorithm

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1801))

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Abstract

Evolutionary multitasking optimization algorithms have been presented for dealing with multiple tasks simultaneously. Many studies have proved that EMTOs often perform better than conventional single-task evolutionary. Transferring knowledge plays a very important role in multitask optimization algorithms. Many existing methods transfer elite solutions between tasks to improve algorithm performance, however, these methods may or produce negative transfer if inter-task similarity is low or irrelevant. This paper presents a self-adaptive multitasking optimization algorithm, SAMTOA, to find more valuable transferred solutions between tasks. In SAMTOA, the solutions for the next generation transfer are adaptively determined based on the successful transfer solutions of the previous generation. The method can effectively reduce the probability of transferring useless solutions between tasks and effectively utilize valuable solutions in tasks to improve the efficiency of knowledge transfer between tasks. Experimental results on single-objective multitasking optimization benchmark problems indicate that SAMTOA outperforms the other the state-of-the-art EMTO algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (No\(\cdot \) 62176146, No\(\cdot \) 62272384), the National Social Science Foundation of China under Grant No\(\cdot \) 21XTY012, the National Education Science Foundation of China under Grant No\(\cdot \) BCA200083, and Key Project of Shaanxi Provincial Natural Science Basic Research Program under Grant 2023−JC−ZD-34.

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Authors and Affiliations

  1. The Key Laboratory of Network Computing and Security Technology of Shaanxi Province, Xi’an University of Technology, Xi’an, 710048, China

    Xiaoyu Li, Lei Wang, Qiaoyong Jiang, Wei Li & Bin Wang

  2. The Key Laboratory of Industrial Automation of Shaanxi Province, Shaanxi University of Technology, Hanzhong, 723001, China

    Lei Wang

  3. School of Electronic and Information Engineering, Ankang University, Ankang, 725000, China

    Xiaoyu Li

Authors
  1. Xiaoyu Li
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  2. Lei Wang
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  3. Qiaoyong Jiang
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  4. Wei Li
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  5. Bin Wang
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Corresponding author

Correspondence to Lei Wang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Wuhan University of Technology, Wuhan, China

    Dongming Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

  4. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, X., Wang, L., Jiang, Q., Li, W., Wang, B. (2023). A Self-adaptive Single-Objective Multitasking Optimization Algorithm. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_10

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  • DOI: https://doi.org/10.1007/978-981-99-1549-1_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1548-4

  • Online ISBN: 978-981-99-1549-1

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