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Multi-granularity competition-cooperation optimization algorithm with adaptive parameter configuration

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Abstract

The intelligent optimization algorithm has the advantage of giving feasible solutions in polynomial time when solving complex problems in reality. Its performance depends on its strategy design and parameter configuration. The key to achieving computational intelligence and self-organization is parameter configuration. However, the parameter configuration in the intelligent optimization algorithm relies on the designer’s experience, and the parameter adjustment process will waste a vast amount of resources. In terms of this issue, this paper proposes a multi-granularity competition-cooperation optimization algorithm with adaptive parameter configuration(MGAP). First, the particles are grouped into groups according to the fitness of the solution. Second, an inter-group competition-cooperation relationship network is established. According to the relationship network, each particle performs adaptive learning between and within the group and updates. Then, reinforcement learning is introduced to train learning rules and learning parameters of adaptive learning between and within groups to promote particle autonomous evolution. We have done comparative experiments with six algorithms on the 10-100 dimension test functions and then give application cases. The comparison results with other algorithms in the optimization function show that the proposed algorithm is computationally effective and efficient in the face of large-scale optimization problems. Further experiments on complex problems in the real scene of 270,000 campus consumption data verify the collaborative optimization performance and collaborative learning performance of our algorithm.

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Acknowledgements

This work was supported in part by the Key Program of the National Natural Science Foundation of China under Grant No. 62136003, the National Natural Science Foundation of China under Grant Nos. 61772200 and 61772201, Shanghai Pujiang Talent Program under Grant No. 17PJ1401900, Shanghai Economic and Information Commission “Special Fund for Information Development” under Grant No. XX-XXFZ-02-20-2463.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Mengqi Gao, Xiang Feng, Huiqun Yu & Zijun Zheng

  2. Shanghai Engineering Research Center of Smart Energy, Shanghai, China

    Mengqi Gao, Xiang Feng, Huiqun Yu & Zijun Zheng

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  1. Mengqi Gao
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  2. Xiang Feng
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  3. Huiqun Yu
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Correspondence to Xiang Feng.

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Gao, M., Feng, X., Yu, H. et al. Multi-granularity competition-cooperation optimization algorithm with adaptive parameter configuration. Appl Intell 52, 13132–13161 (2022). https://doi.org/10.1007/s10489-021-02952-9

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