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Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization

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Abstract

To effectively address large-scale optimization problems, this paper proposes an evolutionary dynamic grouping (EDG) based cooperative co-evolution (CC) algorithm. In the proposed algorithm, a novel decomposition method is designed to generate the sub-components of decision variables dynamically. Additionally, an evolutionary search method based on the fireworks search strategy is proposed to enhance the searchability of the algorithm. The performance of the proposed algorithm is assessed using two benchmark suites, IEEE CEC’2010 and IEEE CEC’2013, as well as a real-world optimization problem, the 0/1 Knapsack Problem (KP). Experimental results demonstrate that the proposed algorithm achieves competitive results when compared with other state-of-the-art algorithms.

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Acknowledgements

The authors are supported by the National Nature Science Foundation of China under Grant No.62273080.

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Authors and Affiliations

  1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

    Wanting Yang, Jianchang Liu, Shubin Tan, Wei Zhang & Yuanchao Liu

  2. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Wanting Yang, Jianchang Liu, Shubin Tan, Wei Zhang & Yuanchao Liu

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Yang, W., Liu, J., Tan, S. et al. Evolutionary dynamic grouping based cooperative co-evolution algorithm for large-scale optimization. Appl Intell 54, 4585–4601 (2024). https://doi.org/10.1007/s10489-024-05390-5

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