Improved Harris Hawks optimization algorithm with multiple strategies

In order to solve the problems of the basic Harris hawks optimization (HHO) that are easy to fall into local optimization and low convergence accuracy, a multi-strategy optimization Harris hawks optimization (MHHO) is proposed.In the exploration stage, the Cauchy distribution function is introduced to mutate the global position which increases population diversity.In the transition stage, the random contraction exponential function is used to nonlinear the energy equation in order to coordinate global exploration and local mining effectively.In the mining stage, the adaptiveweight factor updates the local position and elevates the local mining capacity.By solving multiple single peak, multi peak and high-dimensional test functions, the results show that the MHHO algorithm combining the three strategies has better optimization accuracy and stability.