Advances in Slime Mould Algorithm: A Comprehensive Survey

Author:

Wei Yuanfei12,Othman Zalinda1ORCID,Daud Kauthar Mohd1ORCID,Luo Qifang34,Zhou Yongquan123ORCID

Affiliation:

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

2. Xiangsihu College, Guangxi Minzu University, Nanning 530225, China

3. College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China

4. Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China

Abstract

The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

Funder

National Science Foundation of China

Scientific Research Project of Xiangsihu College of Guangxi Minzu University

Publisher

MDPI AG

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