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A hybrid cooperative differential evolution assisted by CMA-ES with local search mechanism

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Abstract

In this paper, a hybrid cooperative differential evolution with the perturbation of the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) with the local search of Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) mechanism, named jSO_CMA-ES_LBFGS, is proposed to solve the complex continuous problems. In the proposed algorithm, jSO, as a variant of Differential Evolution (DE), is used as a global search operator to explore the entire solution space. When the population falls into stagnation, a relatively reliable initial solution for the local search operator is generated by the CMA-ES, which is activated to perturb the optimal candidates in the solution space. The LBFGS utilized as the local search strategy is embedded in CMA-ES to obtain the potential local optimal solutions. A cooperative co-evolutionary dynamic system is formed by jSO and CMA-ES with a local search operator. The proposed jSO_CMA-ES_LBFGS is tested on the CEC2017 benchmark test suite and compared with eleven state-of-the-art algorithms. Further, two practical engineering problems are investigated utilizing the proposed method. The experimental results reveal the effectiveness and efficiency of the jSO_CMA-ES_LBFGS.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China under grant 62063021. It was also supported by Key talent project of Gansu Province (ZZ2021G50700016), the Key Research Programs of Science and Technology Commission Foundation of Gansu Province (21YF5WA086), Lanzhou Science Bureau project (2018-rc-98), Project of Zhejiang Natural Science Foundation (LGJ19E050001), respectively.

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Fuqing Zhao & Haizhu Bao

  2. Department of Automation, Tsinghua University, Beijing, 10084, China

    Ling Wang

  3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Xuan He

  4. Department of Industrial Engineering, Universitas Andalas, Padang, 25163, Indonesia

    Jonrinaldi

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  1. Fuqing Zhao
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  2. Haizhu Bao
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  3. Ling Wang
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Correspondence to Fuqing Zhao.

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Zhao, F., Bao, H., Wang, L. et al. A hybrid cooperative differential evolution assisted by CMA-ES with local search mechanism. Neural Comput & Applic 34, 7173–7197 (2022). https://doi.org/10.1007/s00521-021-06849-z

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