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

• Published in: Mathematics (Basel) 2024-05, Vol.12 (10), p.1470
• Main Authors: Thakur, Gauri, Pal, Ashok, Mittal, Nitin, Rajiv, Asha, Salgotra, Rohit
• Format: Article
• Language: English
• Subjects: Algorithms; Alkylation; Compression springs; constrained functions; Convergence; Exploitation; Food; Foraging behavior; Gaussian mutation; Genetic algorithms; Learning strategies; Mutation; Optimization; Optimization algorithms; Optimization techniques; Robustness; Slime; slime mould algorithm
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12101470

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.