Aquila optimizer integrating Gaussian walk and somersault strategy

Abstract

To solve the problems of insufficient local search capability and easily falling into local optimization in the Aquila Optimizer (AO), an aquila optimizer integrating Gaussian walk and somersault strategy (AO-IGWSS) is proposed. Strengthening the exploitation ability, a Gaussian walk strategy is used instead of Levy flight to generate step size adaptively controlled by iteration numbers. Furthermore, to enhance the capability of local optima avoidance, a somersault strategy is introduced to update individuals. The experimental results on nine benchmark test functions prove that the AO-IGWSS can achieve better results than the original AO algorithm, the differential evolution mutation and tangent flight aquila optimizer (DEtanAO), and four other intelligent optimization algorithms.