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

Gauri Thakur, Ashok Pal, Nitin Mittal, Asha Rajiv and Rohit Salgotra ()
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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

Mathematics, 2024, vol. 12, issue 10, 1-37

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.

Keywords: optimization; slime mould algorithm; Gaussian mutation; constrained functions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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