Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Area of Science:

• Engineering and Computer Science
• Optimization Techniques
• Computational Intelligence

Background:

• Meta-heuristic algorithms are crucial for solving complex optimization problems in science and engineering.
• The Slime Mould Algorithm (SMA) is a recently developed meta-heuristic algorithm inspired by the foraging behavior of slime mold.
• SMA features a unique mathematical model simulating biological wave-like movement for efficient exploration and exploitation.

Purpose of the Study:

• To investigate the Slime Mould Algorithm (SMA) from various optimization perspectives.
• To analyze the hybridization, progress, changes, and optimization applications of SMA.
• To provide a comprehensive overview of SMA's utility for researchers and practitioners.

Main Methods:

• Review and analysis of existing research on the Slime Mould Algorithm (SMA).
• Categorization of SMA applications into hybridization, progress, changes, and optimization.
• Quantitative assessment of SMA's usage rates in these four areas.

Main Results:

• The Slime Mould Algorithm (SMA) demonstrates significant potential in addressing optimization challenges.
• SMA has been increasingly adopted in scientific research, with applications in hybridization (15%), progress (36%), changes (7%), and optimization (42%).
• The algorithm's unique features, including adaptive weights and a biological wave simulation, contribute to its effectiveness.

Conclusions:

• The Slime Mould Algorithm (SMA) is a valuable and versatile meta-heuristic tool for optimization problems.
• Its unique approach and growing body of research suggest continued relevance and application in engineering and scientific fields.
• This paper serves as a beneficial resource for engineers, professionals, and academic scientists utilizing or exploring SMA.