A Novel Slime Mould Multiverse Algorithm for Global Optimization and Mechanical Engineering Design Problems

• Published in: International journal of computational intelligence systems 2024-12, Vol.17 (1), p.1-56
• Main Authors: Thakur, Gauri, Pal, Ashok
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; CEC2017 functions; Computational Intelligence; Control; Engineering; Engineering problem; Mathematical Logic and Foundations; Mechatronics; Multiverse optimization; Research Article; Robotics; Slime mould algorithm; Statistical tests
• ISSN: 1875-6883
• DOI: 10.1007/s44196-024-00704-4

The slime mould optimization algorithm (SMA) is one of the well-established optimization algorithms with a superior performance in a variety of real-life optimization problems. The SMA has certain limitations that reduce the diversity and accuracy of solutions, raising the risk of premature convergence with an inadequate balance between its exploitation and exploration phases. In this study, a novel hybrid slime mould multi-verse algorithm (SMMVA) is proposed to improve the performance of SMA algorithm. The SMA and multi-verse optimization (MVO) algorithm hybrid is introduced while updating variation parameter through novel nonlinear convergence factor. The proposed algorithm balances the ability of the SMA algorithm to explore and exploit, boosts the global exploration capability and improves the accuracy, stability, and convergence speed. The performance of SMMVA algorithm is compared with 16 well-established and recently-published metaheuristic algorithms on 23 standard benchmark functions, CEC2017, CEC2022 test functions, five engineering design problems, and five UCI repository datasets. The statistical tests such as Friedman’s test, box plot comparison and Wilcoxon rank sum test are employed to verify the SMMVA’s stability and statistical superiority. The algorithm was tested on total 64 benchmark functions, achieving an overall success rate of 68.75% across 30 runs compared to the other counterparts. The results for the feature selection problem show that the proposed algorithm with k-nearest neighbour (KNN) classifier obtained more informative features with higher accuracy values. Thus, the proposed SMMVA algorithm is proven to perform excellent performance in solving optimization problems with better solution accuracy and promising prospect.