Gaussian Perturbation Whale Optimization Algorithm based on Nonlinear Strategy

Whale Optimization Algorithm (WOA) is a recently developed swarm intelligence optimization algorithm that has strong global search capability. In this work, considering the deficiency of WOA in a local search mechanism and convergence speed, a Gaussian Perturbation Whale Optimization Algorithm based on Nonlinear Strategy (GWOAN) is introduced. By implementing a nonlinear change strategy on the parameters, the swarm is able to enter the local search process faster and thus improve the local exploitation ability of the algorithm. In a later stage, Gaussian perturbation is performed on the current optimal individuals to enrich the population diversity, avoid premature convergence of the algorithm, and improve the global development capability of the algorithm. The results of the comparison experiment between the GWOAN, WOA, and PSO algorithms show that the accuracy of GWOAN in the selected ten function optimization solutions is significantly higher than that of the comparison algorithms, and its optimization efficiency is also better. Among the ten benchmark functions, four can converge to the theoretical optimal value. © 2019 Totem Publisher, Inc. All rights reserved.