An Enhanced Whale Optimization Algorithm for the Problems of Function Optimization

To resolve the problem that the whale optimization algorithm (WOA) is easy to fall into local optimum and low precision, an enhanced whale optimization algorithm (EWOA) is proposed. Firstly, the adaptive strategy was introduced into the whale's position to balance the global exploration and local exploitation capabilities of the algorithm, speed up the convergence of the algorithm, and improve the optimization accuracy of the algorithm. Then, to avoid the algorithm falling into local optimum and prevent premature convergence, the idea of differential mutation was introduced to mutate the better whale's position. Finally, the experimental results on nine test functions under fixed parameters and different dimensions show that the improved algorithm has significantly improved search precision and convergence speed compared with the traditional WOA. Especially in the optimization problem of high-dimensional functions, the improved algorithm shows better convergence performance than the traditional WOA and its variants.