Salp swarm algorithm: a comprehensive survey

By Laith Mohammad Abualigah, Mohammad Shehab, Mohammad Alshinwan, Hamzeh Alabool. Neural computing & applications (Print), 2019

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/88a4101a6f12987dbb4e4c727d8ee84976c5728c

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. Seyedali Mirjalili
3. Absalom El-Shamir Ezugwu
4. Amir H. Gandomi
5. Ali Diabat
6. Jeffrey O. Agushaka
7. M. A. Elaziz
8. Ahmed A. Ewees

In Computational Theory and Mathematics

1. Laith Mohammad Abualigah
2. Amir H. Gandomi
3. Seyedali Mirjalili
4. Absalom El-Shamir Ezugwu
5. M. A. Elaziz
6. Jeffrey O. Agushaka
7. Ahmed A. Ewees
8. Ali Diabat

In Electrical and Electronic Engineering

1. Amir H. Gandomi
2. Seyedali Mirjalili
3. Laith Mohammad Abualigah

Citations in the corpus

• Optimization Method for Forecasting Confirmed Cases of COVID-19 in China (2020, methodology): “In recent years, the SSA was utilized to solve different optimization problems, such as feature selection [49,50], data classification [51], image segmentation [52], and others [53,54].”
• Prairie Dog Optimization Algorithm (2022, methodology): “The most popular algo-rithm in this category is particle swarm optimization (PSO); others include the red fox optimization algorithm [35], salp swarm algorithm [36], ant colony optimization [13], and artificial bee colony [14].”
• The Arithmetic Optimization Algorithm (2021, background): “One popular classification of meta-heuristics is based on the inspiration of evolutionary algorithms, swarm intelligence algorithms, physics-based methods, and human-based methods [13,14].”