An Improved Squirrel Search Algorithm With Reproductive Behavior
Xuncai Zhang, Kai Zhao, Lingfei Wang, Yanfeng Wang, Ying Niu
Abstract
The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms.