Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems

Dong, Yuncheng; Tang, Ruichen; Cai, Xinyu

doi:10.3390/biomimetics9080500

Open AccessArticle

Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems

by

Yuncheng Dong

¹,

Ruichen Tang

² and

Xinyu Cai

^3,*

¹

School of Highway and Construction Engineering, Yunnan Communications Vocational and Technical College, Kunming 650500, China

²

College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China

³

College of Business, Jiaxing University, Jiaxing 314001, China

^*

Author to whom correspondence should be addressed.

Biomimetics 2024, 9(8), 500; https://doi.org/10.3390/biomimetics9080500

Submission received: 3 July 2024 / Revised: 6 August 2024 / Accepted: 14 August 2024 / Published: 17 August 2024

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 2nd Edition)

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Abstract

In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search operator in SMA to ensure that the agents can evolve in a better direction during the optimization process. Secondly, the best agent is further modified with an improved non-monopoly search mechanism to boost the algorithm’s exploitation and exploration capabilities. Finally, a random differential restart mechanism is developed to assist SMA in escaping from local optimality and increasing population diversity when it is stalled. The impacts of three strategies are discussed, and the performance of EMSMA is evaluated on the CEC2017 suite and CEC2022 test suite. The numerical and statistical results show that EMSMA has excellent performance on both test suites and is superior to the SMA variants such as DTSMA, ISMA, AOSMA, LSMA, ESMA, and MSMA in terms of convergence accuracy, convergence speed, and stability.

Keywords: Slime Mould Algorithm; non-monopoly search; restart mechanism; numerical optimization; CEC 2017 test suite; CEC 2022 test suite

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MDPI and ACS Style

Dong, Y.; Tang, R.; Cai, X. Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems. Biomimetics 2024, 9, 500. https://doi.org/10.3390/biomimetics9080500

AMA Style

Dong Y, Tang R, Cai X. Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems. Biomimetics. 2024; 9(8):500. https://doi.org/10.3390/biomimetics9080500

Chicago/Turabian Style

Dong, Yuncheng, Ruichen Tang, and Xinyu Cai. 2024. "Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems" Biomimetics 9, no. 8: 500. https://doi.org/10.3390/biomimetics9080500

APA Style

Dong, Y., Tang, R., & Cai, X. (2024). Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems. Biomimetics, 9(8), 500. https://doi.org/10.3390/biomimetics9080500

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Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems

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