Spider wasp optimizer: a novel meta-heuristic optimization algorithm

This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Artificial intelligence review Jg. 56; H. 10; S. 11675 - 11738
Hauptverfasser: Abdel-Basset, Mohamed, Mohamed, Reda, Jameel, Mohammed, Abouhawwash, Mohamed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.10.2023
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Artificial Intelligence
Benchmarks
Computer Science
Design engineering
Exploitation
Heuristic
Heuristic methods
Hunting
Mathematical optimization
Mating
Optimization
Parameter estimation
Pressure vessel design
Pressure vessels
Reliability
Source code
Spiders
Tests
World problems
Metaheuristic
Engineering design problems
Spider wasp optimizer
Stochastic optimization
Constrained optimization
ISSN:0269-2821, 1573-7462
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it applicable to a wide range of optimization problems with different exploration and exploitation requirements. The proposed SWO is compared with nine newly published and well-established metaheuristics over four different benchmarks: (1) Standard benchmark, including 23 unimodal and multimodal test functions; (2) test suite of CEC2017, (3) test suite of CEC2020, and (4) test suite of CEC2014 to validate its reliability. Moreover, two classical engineering design problems, namely, welded bean and pressure vessel designs, and parameter estimation of the single-diode, double-diode, and triple-diode photovoltaic models are used to further evaluate the performance of SWO in optimizing real-world optimization problems. Experimental findings demonstrate that SWO is more competitive compared with the state-of-art meta-heuristic methods for four validated benchmarks and superior to all observed real-world optimization problems. Specifically, SWO achieves an overall effective percentage of 78.2% on the standard benchmark, 92.31% on CEC2014, 77.78% on CEC2017, 60% on CEC2020, and 100% on real-world problems. The source code of SWO is publicly available at  https://www.mathworks.com/matlabcentral/fileexchange/126010-spider-wasp-optimizer-swo .
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10446-y