An Improved Whale Swarm Algorithm with Nonlinear Weighting and Convergence Factor

Abstract

A whale optimization algorithm based on nonlinear convergence factor and inertial weight is proposed to solve the problem of slow convergence and low convergence accuracy. The improved Logistic chaotic map was first used to initialize the swarm and increase the diversity of the swarm. Then the convergence factor of linear change is improved to a piecewise nonlinear convergence factor. Meanwhile, the nonlinear inertial weight is added to enhance the exploration and development ability of the algorithm. In the end, 7 benchmark functions were selected for testing, and the experiment showed that the improved algorithm had fast convergence speed and high precision.