GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering
Challenges and Beyond
Abstract
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
Problem, three renowned real-world engineering challenges, and the
Pathological IgG Fraction in the Nervous System. The outcomes of the
engineering case studies illustrate how well the suggested approach can
optimize issues that arise in the real-world.