Improved Harris hawk algorithm based on multi-strategy synergy mechanism for global optimization

Aiming at the problem that the Harris hawk optimization (HHO) algorithm does not have high optimization accuracy and is prone to fall into local optimum, an improved Harris hawk optimization (SHHO) algorithm based on multi-strategy synergy mechanism is proposed. Firstly, in the initialization stage, out adopts the good point set method to create the population, and draws on the topology theory to divide the population into two different populations to enhance the connection between the populations. Second, a multi-strategy synergy mechanism was designed to randomly divide individuals into two categories: leader eagles and non-leader eagles using the clan topology. The grouping was based on the ordering of fitness values. In addition, a search strategy was constructed for each category to ensure a balance between exploration and exploitation capabilities. Finally, to validate the performance of SHHO, ordinary functions and CEC test set and benchmark functions of CEC test set were selected for simulation and SHHO was compared with other optimization algorithms using ANOVA, Wilcoxon and Friedman tests. Meanwhile, three classical engineering problems are selected to verify the effectiveness of SHHO in real engineering. The results show that SHHO has significant improvement in convergence accuracy and speed, and has high practical value and advantages in engineering optimization applications.