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Adaptive comprehensive learning particle swarm optimization with spatial weighting for global optimization

  • 1213: Computational Optimization and Applications for Heterogeneous Multimedia Data
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Abstract

Although particle swarm optimization (PSO) algorithm shows excellent performance in solving optimization problems, how to balance exploration and exploitation is still a crucial problem. In this paper, an adaptive comprehensive learning particle swarm optimization with spatial weighting (APSO-SW) is proposed. In APSO-SW, in order to increase the population diversity, the Euclidean distance between each particle and the global optimum is calculated in each generation and the particles in the whole population select their exemplars learning weight according to their Euclidean distance adaptively. Therefore, not only different particles have various learning weight, but also the same particle can select learning weight adaptively in different generations. In addition, for the purpose of improving the convergence property, the terminal elimination strategy is used. In terminal elimination strategy, the population can delete inferior particles and add preferable particles dynamically during the process of evolution. The comparisons among APSO-SW and other 7 state-of-the-art PSO variants on the CEC2013 and CEC2017 test suites reflect that APSO-SW is a trustable and remarkable optimization algorithm for solving various types problems. Furthermore, extensive experiments confirm the validity of our method.

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Acknowledgements

The authors thank the Editor and all the anonymous referees for their constructive comments and valuable suggestions, which are helpful to improve the quality of this paper. This work was supported by the National Key R&D Program of China (2019YFF0302203), the National Natural Science Foundation of China (61973067), the Fundamental Research Funds for the Central Universities under Grant N2004006 and the Open Research Fund from the State Key Laboratory of Rolling and Automation, Northeastern University(2019RALKFKT004).

Funding

This work was supported by the National Key R&D Program of China (2019YFF0302203), the National Natural Science Foundation of China (61973067), and the Open Research Fund from the State Key Laboratory of Rolling and Automation, Northeastern University (2019RALKFKT004).

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Authors and Affiliations

  1. Information Science and Engineering, Northeastern University, No.11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People’s Republic of China

    Xu Yang & Hongru Li

  2. The State Key Laboratory of Rolling and Automation, Northeastern University, No.11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People’s Republic of China

    Zhenyu Liu

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  1. Xu Yang
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  2. Hongru Li
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Contributions

XY: Conceptualization, Methodology, Software, Validation, Writing original, draft. HL: Supervision, Writing-review & editing. ZL: Writing-review & editing.

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Correspondence to Hongru Li.

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Yang, X., Li, H. & Liu, Z. Adaptive comprehensive learning particle swarm optimization with spatial weighting for global optimization. Multimed Tools Appl 81, 36397–36436 (2022). https://doi.org/10.1007/s11042-021-11547-y

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