Whale optimization algorithmcombined with dynamic probability threshold anda adaptive mutation

To overcome the deficiencies in optimizing the nonlinear problem of traditional whale optimization algorithm(WOA), including Low convergence accuracyand Easily falling into local optimum in late iteration., an whale optimization algorithm combined with dynamic probability threshold and adaptive mutation (PTMWOA) is proposed. Fuch chaos and opposition-based learning are used to initialize the population which can generate a population of uniform distribution.Dynamic probability threshold of adaptive adjustment is designed to coordinate the exploration and exploitation ability, and variable weight is applied to revise the updating formula for more precise search. Adaptive mutation strategy based is introduced to the optimum whale location to avoid falling into local optimum. Simulation results on 13 benchmark functions show that the proposed algorithm has better performance on solution, and convergence rate than GWO, PSO and WOA algorithms