An improved slime mould algorithm using multiple strategies

Abstract

Aiming at the defects of standard slime mould algorithm (SMA), such as local optima stagnation, slow convergence and improper balance between exploitation and exploration, we propose an improved SMA that contains the adaptive t-distributed variation strategy, improved location update formula and chaotic opposition-based learning strategy, that is, the MISMA. Utilizing comparative experiments and ablation studies on classical benchmark and CEC2020 benchmark suite, we proved that MISMA outperforms other state-of-the-art rival algorithms on convergence speed, solution accuracy, and robustness, each component of MISMA achieves an improvement on the defects of SMA at each stage and exhibits synergistic effects.

GRAPHICAL ABSTRACT

Keywords:

• Improved slime mould algorithm
• chaotic opposition-based learning
• adaptive t-distribution variation strategy
• multiple strategies

Acknowledgments

This article would like to thank the editors and the anonymous referees for their professional comments, which improved the quality of the manuscript.

Disclosure statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (Nos. 12271211, 12071179), the National Natural Science Foundation of Fujian Province (Nos. 2021J01861, 2020J01710), the Youth Innovation Fund of Xiamen City (3502Z20206020), the Project of Education Department of Fujian Province (No. JT180263), Fujian Alliance of Mathematics (2023SXLMMS06), the Open Fund of Digital Fujian Big Data Modeling and Intelligent Computing Institute, Jimei University.