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A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm

Author

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  • Deng, Huaijun
  • Liu, Linna
  • Fang, Jianyin
  • Qu, Boyang
  • Huang, Quanzhen

Abstract

Whale optimization algorithm (WOA), as an advanced optimization algorithm with simple structure, has been favored by various fields. However, there are some disadvantages of WOA, such as slow convergence speed, low precision and falling into local optimal value easily. In this paper, a novel improved whale optimization algorithm (IWOA) with multi-strategy and hybrid algorithm is proposed to overcome above shortcomings. Firstly, IWOA initializes the population by chaotic mapping to avoid the initial population distribution of WOA deviating from the optimal value. Secondly, IWOA combines the pheromone of the black widow algorithm and the opposition-based learning strategy to modify the population, which improves the convergence speed and the global performance of WOA respectively. Finally, the adaptive coefficients and the new update modes replace the original update modes, which makes the structure of WOA simpler and more accurate. In addition, the convergence of IWOA is also proved in this paper. On the one hand, to demonstrate the effectiveness of IWOA, 23 benchmark functions are used to test various performance of the algorithm. On the other hand, in order to prove the superiority of IWOA, the experimental results are compared and analyzed with other optimization algorithms. Simulation results show that IWOA proposed in this paper owns excellent performance in convergence speed, stability, accuracy and global performance, compared with other algorithms.

Suggested Citation

  • Deng, Huaijun & Liu, Linna & Fang, Jianyin & Qu, Boyang & Huang, Quanzhen, 2023. "A novel improved whale optimization algorithm for optimization problems with multi-strategy and hybrid algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 205(C), pages 794-817.
    • Handle: RePEc:eee:matcom:v:205:y:2023:i:c:p:794-817
    DOI: 10.1016/j.matcom.2022.10.023
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    References listed on IDEAS

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    1. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    2. Chinnaraj Govindasamy & Arokiasamy Antonidoss, 2022. "Enhanced Inventory Management Using Blockchain Technology Under Cloud Sector Enabled by Hybrid Multi-Verse with Whale Optimization Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 21(02), pages 577-614, March.
    3. Tawhid, M.A. & Ibrahim, A.M., 2021. "Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1342-1369.
    4. Sorin-Mihai Grad & Felipe Lara, 2021. "Solving Mixed Variational Inequalities Beyond Convexity," Journal of Optimization Theory and Applications, Springer, vol. 190(2), pages 565-580, August.
    5. Mahnaz Toloueiashtian & Mehdi Golsorkhtabaramiri & Seyed Yaser Bozorgi Rad, 2022. "An improved whale optimization algorithm solving the point coverage problem in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(3), pages 417-436, March.
    6. Wumei Sun & Hongwei Liu & Zexian Liu, 2021. "A Class of Accelerated Subspace Minimization Conjugate Gradient Methods," Journal of Optimization Theory and Applications, Springer, vol. 190(3), pages 811-840, September.
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    Cited by:

    1. Xing, Aosheng & Chen, Yong & Suo, Jinyi & Zhang, Jie, 2024. "Improving teaching-learning-based optimization algorithm with golden-sine and multi-population for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 221(C), pages 94-134.

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