Comparison of Black Widow Optimization and Aquila Optimizer with Current Metaheuristics

Abstract

Metaheuristic optimization algorithms are an optimization approach that produces acceptable solutions in situations where it is difficult to create a mathematical model in an optimization problem or in large-scale, multivariate optimization problems. Metaheuristics play a significant role in solving optimization problems. In this study, five current meta- heuristics (Aquila Optimizer (AO), Artificial Rabbits Optimization (ARO), Black Widow Optimization (BWO), Harris Hawk Optimization (HHO) and Sooty Tern Optimization Algorithm (STOA), which are inspired by swarm intelligence and foraging behavior of creatures in nature) are compared. These algorithms are discussed in detail and information is given about their working principles. As far as is known, this is the first time that the performances of these five algorithms have been compared. The algorithms were evaluated with unimodal and multimodal test functions. The simulation results demonstrate that AO and BWO are more successful than the other algorithms. It is also evaluated that the metaheuristics used in the study can be applied to many engineering problems.

Keywords

• Metaheuristics
• Aquila Optimization
• Artificial Rabbit Optimization
• Black Widow Optimization
• Harris Hawk Optimization
• Sooty Tern Optimization Algorithm

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