A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems

Harris hawks optimization (HHO) is a recently developed meta-heuristic optimization algorithm based on hunting behavior of Harris hawks. Similar to other meta-heuristic algorithms, HHO tends to be trapped in low diversity, local optima and unbalanced exploitation ability. In order to improve the performance of HHO, a novel quasi-reflected Harris hawks algorithm (QRHHO) is proposed, which combines HHO algorithm and quasi-reflection-based learning mechanism (QRBL) together. The improvement includes two parts: the QRBL mechanism is introduced firstly to increase the population diversity in the initial stage, and then, QRBL is added in each population position update to improve the convergence rate. The proposed method will also be helpful to control the balance between exploration and exploitation. The performance of QRHHO has been tested on twenty-three benchmark functions of various types and dimensions. Through comparison with the basic HHO, HHO combined with opposition-based learning mechanism and HHO combined with quasi-opposition-based learning mechanism, the results demonstrate that QRHHO can effectively improve the convergence speed and solution accuracy of the basic HHO and two variants of HHO. At the same time, QRHHO is also better than other swarm-based intelligent algorithms.