Slime mould algorithm: A new method for stochastic optimization

By Shimin Li, Huiling Chen, Mingjing Wang, Ali Asghar Heidari, Seyedali Mirjalili. Future generations computer systems, 2020

2,690 citations recorded by the source corpus.

Abstract In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based on the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics using an extensive set of benchmarks to verify its efficiency. Moreover, four classical engineering problems are utilized to estimate the efficacy of the algorithm in optimizing constrained problems. The results demonstrate that the proposed SMA benefits from competitive, often outstanding performance on different search landscapes. The source codes of SMA are publicly available at http://www.alimirjalili.com/SMA.html and https://tinyurl.com/Slime-mould-algorithm .

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1b1c9b31dde42a8818a255866db50b0a026b106e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Laith Mohammad Abualigah
2. Ali Asghar Heidari
3. Huiling Chen
4. Absalom El-Shamir Ezugwu
5. Amir H. Gandomi
6. Iman Ahmadianfar
7. Olaide Nathaniel Oyelade
8. Tehnan I. A. Mohamed

In Biomedical Engineering

1. Amir H. Gandomi
2. Ali Asghar Heidari
3. Huiling Chen
4. Saeed Noshadian
5. Laith Mohammad Abualigah
6. Iman Ahmadianfar
7. Arpan Kumar Kar

In Computer Vision and Pattern Recognition

1. Ali Asghar Heidari
2. Laith Mohammad Abualigah
3. Amir H. Gandomi
4. Huiling Chen
5. Saeed Noshadian
6. Absalom El-Shamir Ezugwu
7. Iman Ahmadianfar
8. Ali Diabat

Citations in the corpus

• INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “GA, DE, PSO, artificial bee colony (ABC) [34], ant colony optimization (ACO) [35-37], slime mould algorithm (SMA)(2) [38], and Harris hawks optimization (HHO) 3 [39-41] are some of the population-based algorithms.”