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A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems

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  • Chen, Huiling
  • Wang, Mingjing
  • Zhao, Xuehua

Abstract

The Sine Cosine Algorithm (SCA) has received much attention from engineering and scientific fields since it was proposed. Nevertheless, when solving multimodal or complex high dimensional optimization tasks, the conventional SCA still has a high probability of falling into the local optimal stagnation or failing to obtain the global optimum solution. Additionally, it performspoorly in convergence. Therefore, in this study, a multi-strategy enhanced SCA, a memetic algorithm termed MSCA, is proposed, which combines multiple control mechanisms including Cauchy mutation operator, chaotic local search mechanism, opposition-based learning strategy and two operators based on differential evolution to achieve a better balance between exploration and exploitation. To verify its performance, MSCA was compared with 11 state-of-the-art original optimizers and variant algorithms on 23 continuous benchmark tasks including 7 unimodal tasks, 6 multimodal tasks, 10 various fixed-dimension multimodal functions, and several typical CEC2014 benchmark problems. Furthermore, MSCA was utilized to solve three constrained practical engineering problems including tension/compression spring design, welded beam design, and pressure vessel design. The experimental results demonstrate that the proposed algorithm MSCA is superior to other competitors in terms of quality of solutions and convergence speed and can serve as an effective andefficient computer-aided tool for practical tasks with complex search space.

Suggested Citation

  • Chen, Huiling & Wang, Mingjing & Zhao, Xuehua, 2020. "A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
  • Handle: RePEc:eee:apmaco:v:369:y:2020:i:c:s0096300319308641
    DOI: 10.1016/j.amc.2019.124872
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    Citations

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    Cited by:

    1. Yu, Caiyang & Cai, Zhennao & Ye, Xiaojia & Wang, Mingjing & Zhao, Xuehua & Liang, Guoxi & Chen, Huiling & Li, Chengye, 2020. "Quantum-like mutation-induced dragonfly-inspired optimization approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 259-289.
    2. Ren, Hao & Li, Jun & Chen, Huiling & Li, ChenYang, 2021. "Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 380-409.
    3. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    4. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.
    5. Jian Zhao & Bochen Zhang & Xiwang Guo & Liang Qi & Zhiwu Li, 2022. "Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-31, November.
    6. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    7. Laith Abualigah & Ali Diabat & Davor Svetinovic & Mohamed Abd Elaziz, 2023. "Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2693-2728, August.
    8. Akram Belazi & Héctor Migallón & Daniel Gónzalez-Sánchez & Jorge Gónzalez-García & Antonio Jimeno-Morenilla & José-Luis Sánchez-Romero, 2022. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization," Mathematics, MDPI, vol. 10(7), pages 1-47, April.
    9. Jiao, Shan & Chong, Guoshuang & Huang, Changcheng & Hu, Hanqing & Wang, Mingjing & Heidari, Ali Asghar & Chen, Huiling & Zhao, Xuehua, 2020. "Orthogonally adapted Harris hawks optimization for parameter estimation of photovoltaic models," Energy, Elsevier, vol. 203(C).
    10. Yu Li & Xiaoxiao Lin & Jingsen Liu, 2021. "An Improved Gray Wolf Optimization Algorithm to Solve Engineering Problems," Sustainability, MDPI, vol. 13(6), pages 1-23, March.

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