A Novel Brownian Motion-based Hybrid Whale Optimization Algorithm

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