Nonlinear Inertia Weight Whale Optimization Algorithm with Multi-strategy and Its Application

Abstract

Whale optimization algorithm (WOA) suffers from slow convergence speed, low convergence accuracy, also difficulty in escaping local optima. To address these issues, we propose an improved whale algorithm that incorporates Latin hypercube sampling for population initialization. This ensures a more uniform distribution of the population in the initial stage compared to the random initialization. And then, introducing Cauchy Distribution into WOA’s searching prey stage. This prevents premature convergence to local optima and avoids affecting the later convergence. Furthermore, applying a nonlinear inertia weight to make an improvement on the convergence speed and accuracy of the algorithm. And compared the improved whale algorithm with other algorithms using 18 benchmark functions, and all results given indicated that the proposed algorithm better than original WOA and other algorithms. Finally, applying the improved WOA, original WOA and a mutate WOA to optimize the design of a pressure vessel, and the optimization results demonstrated that the effectiveness of the proposed method.