<p>Harris Hawk optimization (HHO) is an intelligent optimization algorithm that simulates the predatory behavior of Harris Hawk. In order to further improve its convergence speed and convergence accuracy, an improved HHO based on the improved escaping energy factors and random search strategy was proposed. Firstly, five different escaping energy factors were proposed to reduce global search time and improve its convergence speed and convergence accuracy. The improved escaping energy factors can better balance the global and local search performance. Secondly, five random searching strategies (cotangent exponential distribution flight operator, cotangent Wei-bull distribution flight operator, inverse sine flight operator, Cauchy inverse cumulative exponential distribution operator, and cotangent log-normal distribution flight operator) were proposed in the local search by adding some large step size, which in turn enhances the searching mechanism and convergence speed. These five random search strategies enhance the global search capability while speeding up the local search velocity. Thirdly, by using 30 benchmark test functions in CEC-2017, the above improvements are sequentially compared with the original HHO. The best-improved algorithm, ECEHHO, is selected and compared with AOA, BOA, RSA, BAT, RSO, COA, and other improved HHO to verify its convergence performance. Finally, the improved HHO algorithm solves four engineering optimization design problems. The simulation experiments results show that the proposed improved HHO algorithm has the characteristics of balancing exploration and exploitation, fast convergence and high accuracy.</p>