To strengthen global exploration and local exploitation capacity of harris hawks optimization(HHO), an improved HHO(IHHO)algorithm is proposed by coupling the energy cycle decline mechanism and the Newton local enhancement strategy. On the basis of the canonical HHO algorithm, a control coefficient of the prey energy cycle decline mechanism is designed and absorbed into the original prey energy function, which inspires multi-round besiege- escaping phenomena between hanks and prey in nature. On the whole, the prey energy is declining with iterations. This mechanism contributes to dynamically balancing the global and local searching ability of HHO. Meanwhile, in consideration of better property of Newton iteration thought, a kind of Newton local reinforcement strategy is constructed for re-exploiting the local neighbourhood of prey(current optima), which results in improving the local searching performance of the IHHO algorithm with probability. Experimental results show the performance difference influence of different number of cycles and local searching on HHO, the superior parallel iterative optimization ability and convergence accuracy of the proposed algorithm, and its better applicability on high dimension cases(100D-10000D).