Abstract: The wind driven optimization (WDO) algorithm is a population-based iterative heuristic global optimization algorithm.However,in order to deal with the problem that WDO algorithm is easily trapped into local optima,we introduced five WDO algorithms based on different mutation strategies.They are wavelet mutation strategy,chaotic mutation strategy,non-uniform mutation strategy,Gaussian mutation strategy and Cauchy mutation strategy.Different WDO mutation strategies were used to implement simulation experiments for several typical test functions and compared with particle swarm optimization (PSO) algorithm.Experiments show that the WDO with wavelet mutation (WDOWM) algorithm has a strong developing ability,which has capability to jump out of the local optima.The WDOWM algorithm is superior to the PSO algorithm,WDO algorithm and other improved WDO algorithms in terms of convergence rate,convergence accuracy and stability.

Key words: Wind driven optimization algorithm,Mutation,Global optimization