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Multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism

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Abstract

Multifactorial optimization (MFO) is a newly developed optimization framework that can be embedded with an evolutionary algorithm to solve multiple optimization tasks simultaneously. To further explore the generality of the MFO framework, this paper first attempts to use teaching-learning-based optimization as a base optimizer for multiple optimization tasks (MFTLBO). However, the quality of the solution obtained by MFTLBO is not satisfactory because of premature convergence. Therefore, an MFTLBO variant named multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism (DTMFTLBO) is proposed to reduce premature convergence. The diversity indicator is used to dynamically adjust the exploration and exploitation. The cooperative teaching strategy based on weight is designed to acquire evolution direction information, which can guide the population toward more promising solution regions. The triangle cooperation strategy is employed to promote the knowledge transfer between different tasks in the learner phase, which can overcome premature convergence. To verify the efficiency of the proposed DTMFTLBO, numerical studies have been conducted with the 9 commonly used single objective benchmark problems of MTO. Experimental results show that the average excellent rate of the DTMFTLBO for 18 tasks is 85.80%, which confirms the competitiveness of our proposed algorithm compared with eight state-of-the-art multitask optimization algorithms.

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Acknowledgments

This research is partly supported by the National Natural Science Foundation of China under Project Code (61803301, 61773314), and the Scientific Research Foundation of the National University of Defense Technology (grant no. ZK18-03-43).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Wei Li, Yaochi Fan & Qiaoyong Jiang

  2. Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an, China

    Lei Wang

  3. College of Information and Communication, National University of Defense Technology, Xi’an, China

    Qingzheng Xu

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  1. Wei Li
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  2. Yaochi Fan
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  3. Lei Wang
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  4. Qiaoyong Jiang
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  5. Qingzheng Xu
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Correspondence to Wei Li.

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Li, W., Fan, Y., Wang, L. et al. Multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism. Appl Intell 52, 16512–16531 (2022). https://doi.org/10.1007/s10489-021-03059-x

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