Improved salp swarm algorithm based on particle swarm optimization for feature selection

By Rehab Ali Ibrahim, Ahmed A. Ewees, Diego Oliva, M. G. A. Aziz, Songfeng Lu. Journal of Ambient Intelligence and Humanized Computing, 2018

323 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/626f502b8fb8d6e7a0a36c13f27640088de7c2c9

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. Amir H. Gandomi
5. Iman Ahmadianfar
6. Mohammad Shehab
7. Mohammad Alshinwan
8. Hamzeh Alabool

Citations in the corpus

• Salp swarm algorithm: a comprehensive survey (2019, methodology): “A hybrid optimization method is proposed in [38] for solving the feature selection problem.”
• INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, background): “Currently, PSO has taken its place in the fields of large-scale optimisation problems[19], feature selection[20], single-objective optimization problem[21], multiobjective optimisation problems[22], and high-dimensional expensive problem[23].”