An improved gray wolf optimization algorithm and its application in iron removal from zinc sulfate solution using goethite

摘要

The Gray Wolf Optimization (GWO) algorithm has disadvantages including low solution accuracy and being easily trapped in the local optimum. In order to overcome these disadvantages, an Improved Gray Wolf Optimization (IGWO) algorithm is proposed. The research applies chaos theory to generate the initial population to ensure that more individuals are distributed in the searching space. A non-linear convergence factor described based on the logarithmic function is proposed to replace the linear decreasing convergence factor so as to coordinate the exploration and development capacities of the algorithm. In order to avoid occurrence of the premature convergence of the algorithm, a mutation operator with adaptable mutation probability is introduced. The numerical experimental results of four benchmark test functions and the data simulation results in the removal of irons from zinc sulfate solution using goethite show that the IGWO algorithm has a favorable optimization performance.