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Volume 10 Issue 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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S. Gupta, S. Singh, R. Su, S. Gao, and  J. C. Bansal,  “Multiple elite individual guided piecewise search-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 135–158, Jan. 2023. doi: 10.1109/JAS.2023.123018
Citation: S. Gupta, S. Singh, R. Su, S. Gao, and  J. C. Bansal,  “Multiple elite individual guided piecewise search-based differential evolution,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 135–158, Jan. 2023. doi: 10.1109/JAS.2023.123018

Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

doi: 10.1109/JAS.2023.123018
Funds:  This work was supported by the A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre-Positioning (IAF-PP) (Award A19D6a0053) and the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP22H03643)
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  • The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters’ selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

     

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    Highlights

    • A new Differential Evolution (DE) variant called MIDE is proposed
    • The MIDE proposed a new mutation operator to perform a piecewise search in DE
    • Control parameters are tuned based on their success rates in past evolutionary stages
    • Extensive experiments and comparisons validate the efficiency of the MIDE

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