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Updated: Jul 5, 2025

A Method for Growing Bio-memristors from Slime Mold

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

Advances in Slime Mould Algorithm: A Comprehensive Survey.

Yuanfei Wei^1,2, Zalinda Othman¹, Kauthar Mohd Daud¹

¹Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Biomimetics (Basel, Switzerland)

|January 22, 2024

Summary

This summary is machine-generated.

This review details the slime mould algorithm (SMA), a swarm intelligence technique. It covers SMA theory, variants, and applications in optimization and machine learning, suggesting future research directions.

Keywords:

metaheuristic algorithm optimization slime mould algorithm (SMA)swarm intelligence

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Area of Science:

Computational Intelligence
Swarm Intelligence
Optimization Algorithms

Background:

The slime mould algorithm (SMA) is a nature-inspired metaheuristic.
It mimics slime mould foraging behavior.
SMA has gained significant research interest due to its effectiveness.

Purpose of the Study:

To provide a comprehensive review of the slime mould algorithm (SMA).
To summarize recent advancements and applications of SMA.
To identify future research opportunities for SMA.

Main Methods:

Systematic literature review of 130 articles from Google Scholar (2022-2023).
Description of the core SMA theory.
Categorization of SMA variants based on improvement strategies.

Main Results:

Detailed explanation of the SMA's theoretical underpinnings.
Classification of various improved SMA variants.
Overview of SMA's application domains including engineering, energy, machine learning, networking, scheduling, and image segmentation.

Conclusions:

The slime mould algorithm is a valuable tool in various optimization and AI tasks.
Further research is recommended for multi-objective, discrete, and neural network applications of SMA.
The review highlights SMA's potential for continued development and broader application.