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Multiple search operators selection by adaptive probability allocation for fast convergent multitask optimization

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Abstract

Evolutionary multitask optimization (EMTO) has developed fast recently, and many algorithms have emerged that solve several different problems simultaneously through knowledge transfer. Most algorithms use a single search operator in their processing. However, different tasks have distinct characteristics, and a single operator is often inadequate to adapt to different stages of the same task. In this paper, we propose a multiple search operator selection strategy by adaptive probability allocation, named adaptive multi-operator selection (AMOS) to address EMTO that features rapid convergence of populations. It can automatically select the best multiple search operators based on the characteristics of specific tasks and different stages of evolution. The primary contributions of the proposed algorithm are as follows: (1) It combines the basic concepts of multi-operator integration and adaptive search operator selection to select the best multiple search operators for each task at different evolutionary stages; (2) It facilitates the knowledge transfer through different solving operators between tasks; (3) It can be flexibly embedded into various frameworks of general EMTO algorithms with good results. In the experiments, we validate the performance of AMOS on CEC2017 benchmark suite, CMTOPs benchmark suite, and real-world EMTO problems, and experimental results demonstrate the effectiveness and generality of the proposed strategy.

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Data Availability

The dataset analyzed during the current study is available from the [MTO-Platform] repository: [https://github.com/intLyc/MTO-Platform]. And AUC data from [https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/]

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Funding

This work was supported by: (1) the National Natural Science Foundation of China under Grant 62176146, 62272384; (2) the National Social Science Foundation of China under Grant 21XTY012; (3) the National Education Science Foundation of China under Grant BCA200083; (4) 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

    Zhaoqi Wang, Lei Wang, Qiaoyong Jiang, Xinhui Duan, Zhennan Wang & Liangliang Wang

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

    Lei Wang

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  1. Zhaoqi Wang
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  2. Lei Wang
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Contributions

All the authors contributed to the conception and design of the study. Material preparation, data collection and analysis were performed by ZW, XD, LW and ZW. The first draft of the manuscript was written by LW, ZW and QJ, who commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Lei Wang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical and Informed Consent for the Utilized Data

The dataset used is open source and can be accessed at [https://github.com/intLyc/MTO-Platform].

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Wang, Z., Wang, L., Jiang, Q. et al. Multiple search operators selection by adaptive probability allocation for fast convergent multitask optimization. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06016-w

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