An Improved Whale Optimization Algorithm Based on Nonlinear Function and Local Search

Abstract

In order to improve the searching ability of whale optimization algorithm in continuous optimization function, an improved whale optimization algorithm based on nonlinear function and local search (NLWOA) is proposed. First, because the linear decreasing convergence function cannot balance the exploitation and exploration ability of WOA, this paper designs a nonlinear convergence function to make the algorithm have outstanding exploitation ability in the early stage and excellent exploration ability in the later stage. Second, the original whale optimization algorithm is too divergent in the random search stage. Thus, this paper introduces the historical optimization of whale population and individual. Finally, the proposed algorithm is tested in 23 benchmark functions and compared with other optimization algorithms. The experimental results show that NLWOA can better balance the exploitation and exploration capabilities. So NLWOA has better optimization capability.