  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.
