A Novel Brownian Motion-based Hybrid Whale Optimization Algorithm

Abstract

The Whale Optimization Algorithm (WOA) has the characteristics of simple implementation and few adjustment parameters, which is remarkable in the optimization algorithm. However, there are shortcomings such as premature convergence, slow convergence in the later period, and low search accuracy. For these shortcomings, a novel Brownian motion-based hybrid whale optimization algorithm (HWOA) is proposed. The search strategy in the Harris hawk optimization algorithm (HHO) is adopted to improve the global search ability of the algorithm, and a soft besiege with progressive rapid dives is introduced to solve the problems of premature convergence and slow convergence. Besides, the Brownian motion model is used to replace WOA. The random parameters in the distance formula are calculated to better simulate the prey’s escape during the predation process, and help to jump out of the local optimum. The simulation of 23 benchmark functions shows that compared with the classic and HWOA and metaheuristic, the convergence accuracy and speed have been improved, and the local optimum can be effectively jumped out. At the same time, 10 CEC06-2019 test functions are used to test and analyze it. Compared with WOA, HWOA has better search results, which verifies the superiority of the improved algorithm.