GOOSE algorithm: a powerful optimization tool for real-world engineering challenges and beyond

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark...

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Veröffentlicht in:Evolving systems Jg. 15; H. 4; S. 1249 - 1274
Hauptverfasser: Hamad, Rebwar Khalid, Rashid, Tarik A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Schlagworte:
Animals
Artificial Intelligence
Benchmarks
Comparative studies
Complex Systems
Complexity
Design optimization
Engineering
Genetic algorithms
Heuristic methods
Nervous system
Optimization algorithms
Optimization techniques
Original Paper
Particle swarm optimization
Pressure vessel design
Pressure vessels
Metaheuristic optimization algorithms
Real-World engineering challenges
Benchmark test functions
Pathological IgG fraction in the nervous system
GOOSE algorithm
Evaluation study
ISSN:1868-6478, 1868-6486
Online-Zugang:Volltext
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Zusammenfassung:This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problems, Pressure vessel Design Problems, and the Pathological IgG Fraction in the Nervous System, four renowned real-world challenges. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world.
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ISSN:1868-6478
1868-6486
DOI:10.1007/s12530-023-09553-6