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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12101470