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Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems

Author

Listed:
  • Gauri Thakur

    (Department of Mathematics, Chandigarh University, Mohali 140413, Punjab, India)

  • Ashok Pal

    (Department of Mathematics, Chandigarh University, Mohali 140413, Punjab, India)

  • Nitin Mittal

    (Department of Industry 4.0, Shri Vishwakarma Skill University, Palwal 121102, Haryana, India)

  • Asha Rajiv

    (Department of Physics & Electronics, School of Sciences, JAIN (Deemed to Be University), Bangalore 560069, Karnataka, India)

  • Rohit Salgotra

    (Faculty of Physics and Applied Computer Science, AGH University of Krakow, 30-059 Krakow, Poland
    MEU Research Unit, Middle East University, Amman 11813, Jordan)

Abstract

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value.

Suggested Citation

  • Gauri Thakur & Ashok Pal & Nitin Mittal & Asha Rajiv & Rohit Salgotra, 2024. "Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems," Mathematics, MDPI, vol. 12(10), pages 1-37, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1470-:d:1391261
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    References listed on IDEAS

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    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. Martin Schlüter & Matthias Gerdts, 2010. "The oracle penalty method," Journal of Global Optimization, Springer, vol. 47(2), pages 293-325, June.
    Full references (including those not matched with items on IDEAS)

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