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Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems.

Yuncheng Dong¹, Ruichen Tang², Xinyu Cai³

¹School of Highway and Construction Engineering, Yunnan Communications Vocational and Technical College, Kunming 650500, China.

Biomimetics (Basel, Switzerland)

|August 28, 2024

Summary

This summary is machine-generated.

The Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) improves optimization by incorporating leader covariance learning, enhanced non-monopoly search, and a random restart mechanism. EMSMA demonstrates superior performance over existing Slime Mould Algorithm variants.

Keywords:

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

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Area of Science:

Computational Intelligence
Metaheuristic Optimization
Swarm Intelligence

Background:

The Slime Mould Algorithm (SMA) is a bio-inspired metaheuristic algorithm known for its effectiveness in optimization problems.
Existing SMA variants have limitations in convergence speed, exploration-exploitation balance, and escaping local optima.

Purpose of the Study:

To introduce the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) to overcome the limitations of the original SMA.
To enhance the exploration and exploitation capabilities of the Slime Mould Algorithm.
To improve the algorithm's ability to escape local optima and maintain population diversity.

Main Methods:

Incorporation of a leader covariance learning strategy to guide agent evolution.
Modification of the best agent with an improved non-monopoly search mechanism.
Development of a random differential restart mechanism to address stalled states and enhance diversity.

Main Results:

EMSMA was evaluated on the CEC2017 and CEC2022 test suites.
Numerical and statistical analyses confirmed EMSMA's excellent performance.
EMSMA outperformed several SMA variants (DTSMA, ISMA, AOSMA, LSMA, ESMA, MSMA) in convergence accuracy, speed, and stability.

Conclusions:

The proposed EMSMA effectively enhances the performance of the Slime Mould Algorithm.
The integrated strategies significantly improve optimization capabilities, including convergence and robustness.
EMSMA represents a superior alternative to existing SMA variants for complex optimization tasks.