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An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimization

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Abstract

Large scale multi-objective optimization problems often involve hundreds or thousands of decision variables. Regular methods tend to divide decision variables into multiple groups by identifying the contributions to objectives. However, they may suffer from a large computational budget prior to the start of optimization, resulting in a less computational budget for the actual optimization of problems. Different from them, this paper proposes an adaptive variance vector strategy, which is able to identify convergence-related and diversity-related variables by the variance features of variables in the decision space. The adaptive variance vector not only consumes no additional computational budget, but also is proved to be empirically effective in categorizing decision variables. Based on the adaptive variance vector strategy, an adaptive variance vector-based evolutionary algorithm is designed for tackling large scale multi-objective optimization. Experimental results and empirical analyses on LSMOP and DTLZ test suites with up to 5000 decision variables demonstrate the effectiveness of the adaptive variance vector strategy in identifying the convergence-related and diversity-related variables, and the superiority of the proposed method over state-of-the-art methods in terms of the convergence and diversity.

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Acknowledgements

This work was supported by the Research Start-up Foundation for High-level Talents of Henan University of Technology (No. 31401485), Science and Technology Research Project of Henan Province (No. 232102210042), Innovation Fund Project of Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, China (Nos. 1221047, 1221046), National Natural Science Foundation of China (No. 62273263,62006071), Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT20003), Shanghai Municipal Science and Technology Major Project, Shanghai (No. 2021SHZDZX0100), Fundamental Research Funds for the Central Universities, Science and Technology Project of Suzhou, China (No. SS202151), Program to Cultivate Middle-aged and Young Cadre Teacher of Jiangsu Province, China, and the Science and Technology Project of Science and Technology Department of Henan Province (No. 212102210149).

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Authors and Affiliations

  1. College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, 450001, Henan, China

    Maoqing Zhang & Yashuang Mu

  2. Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education, Beijing, 100039, China

    Maoqing Zhang

  3. Wenzhou Polytechnic, Wenzhou, 325000, Zhejiang, China

    Wuzhao Li

  4. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Hao Jin

  5. Jiangsu Province Support Software Engineering R &D Center for Modern Information Technology Application in Enterprise, Suzhou, 215104, China

    Liang Zhang

  6. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Liang Zhang & Lei Wang

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  1. Maoqing Zhang
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Zhang, M., Li, W., Jin, H. et al. An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimization. Neural Comput & Applic 35, 16357–16379 (2023). https://doi.org/10.1007/s00521-023-08505-0

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