Biogeography Based optimization with Salp Swarm optimizer inspired operator for solving non-linear continuous optimization problems

In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are give...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Alexandria engineering journal Jg. 73; S. 321 - 341
Hauptverfasser: Garg, Vanita, Deep, Kusum, Alnowibet, Khalid Abdulaziz, Zawbaa, Hossam M., Mohamed, Ali Wagdy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 15.07.2023
Elsevier
Schlagworte:
Biogeography Based Optimization
Exploitation
Exploration
Salp Swarm Algorithm
Stochastic algorithms
Stochastic algorithms
Biogeography Based Optimization
Exploration
Salp Swarm Algorithm
Exploitation
ISSN:1110-0168
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are given. The proposed algorithm is also applied to 10 real life problems and compared with its counterpart algorithm. Results obtained by above experiments have demonstrated the outperformance of the hybrid version of BBO over other algorithms.
ISSN:1110-0168
DOI:10.1016/j.aej.2023.04.054