Abstract
Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers.