A Modified Whale Optimization Algorithm

Abstract

To overcome the slow speed and low precision in convergence of the Whale Optimization Algorithm(WOA),to preserve the simplicity of the original algorithm while enhancing the performance,an improved WOA is proposed.Firstly,to maintain the diversity of the initial population in the global search,the population position is initialized by the chaotic sequence generated by the piecewise Logistic chaotic mapping.Secondly,considering the nonlinear optimization process of the algorithm and the difference of individual state in the search process,a nonlinear adaptive weighting strategy is introduced in the basic algorithm to coordinate the global exploration and local development.By the simulation,it compares the performance of the improved algorithm and the WOA on solving six typical benchmark functions.Experimental results show that the improved WOA preserves the initial population diversity in the process of optimization with better convergence speed and precision.