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Improved salp swarm algorithm combined with chaos

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  • Tawhid, Mohamed A.
  • Ibrahim, Abdelmonem M.

Abstract

A recently developed metaheuristic optimization algorithm, Salp Swarm Algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps’ swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders’ attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results’ significance.

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  • Tawhid, Mohamed A. & Ibrahim, Abdelmonem M., 2022. "Improved salp swarm algorithm combined with chaos," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 113-148.
    • Handle: RePEc:eee:matcom:v:202:y:2022:i:c:p:113-148
    DOI: 10.1016/j.matcom.2022.05.029
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    References listed on IDEAS

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    1. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac, 2021. "Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm," Energies, MDPI, vol. 14(5), pages 1-20, February.
    2. El-Fergany, Attia A., 2018. "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer," Renewable Energy, Elsevier, vol. 119(C), pages 641-648.
    3. Narinder Singh & Le Hoang Son & Francisco Chiclana & Jean-Pierre Magnot, 2020. "A new fusion of salp swarm with sine cosine for optimization of non-linear functions," Post-Print hal-02497137, HAL.
    4. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
    5. 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.
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    Cited by:

    1. Shahad Ibrahim Mohammed & Nazar K. Hussein & Outman Haddani & Mansourah Aljohani & Mohammed Abdulrazaq Alkahya & Mohammed Qaraad, 2024. "Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization," Mathematics, MDPI, vol. 12(2), pages 1-39, January.
    2. Andrei M. Tudose & Dorian O. Sidea & Irina I. Picioroaga & Nicolae Anton & Constantin Bulac, 2023. "Increasing Distributed Generation Hosting Capacity Based on a Sequential Optimization Approach Using an Improved Salp Swarm Algorithm," Mathematics, MDPI, vol. 12(1), pages 1-22, December.
    3. Zhiqiang Liu & Weidong Wang & Junyi He & Jianjun Zhang & Jing Wang & Shasha Li & Yining Sun & Xianyang Ren, 2023. "A New Hybrid Algorithm for Vehicle Routing Optimization," Sustainability, MDPI, vol. 15(14), pages 1-15, July.

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