A novel hybrid Aquila optimizer with Whale optimziation algorithm for global optimization, feature selection and optimizing SVM parameters

Abstract

“Support vector machine (SVM)” is one of the well-known machine learning algorithms used for classification and regression tasks. The “SVM” parameters and features selection have a significant impact on the “SVM” model's complexity and classification accuracy. Finding the best set of features and selecting the appropriate parameters of “SVM” is considered an optimization problem. Different metaheuristic algorithms are employed in the literature to choose the best set of features and to optimize “SVM” parameters simultaneously. This paper presents a new algorithm hybrid based on “Aquila Optimizer” and “Whale Optimization Algorithm” with Adaptive inertial weight, called AOWOA. The AOWOA is used for global optimization and feature selection and optimizing “SVM” parameters to achieve a higher classification accuracy. We utilized two experiment series to test the presented (AOWOA) algorithm. In the first experiment, 13 standard “benchmark functions” are used and the AOWOA algorithm is compared to AO, MVO, WOA, MFO, PSO, and SSA. In the second experiment, the results of AOWOA-SVM are compared with four metaheuristic algorithms: MVO, GWO, WOA, and BAT using ten labeled datasets. The experiment results proved that AOWOA can reduce the number of features, find the optimal parameters of “SVM”, and avoids local optima with high classification accuracy in most datasets compared to other algorithms.