Abstarct: Optimization methods inspired by nature are among the smart optimization methods that have shown significant success along with other classical methods. Examples include genetic algorithms (inspired by the biological evolution of humans with other organisms), ant colony (baxsed on the optimal movement of ants) and the simulated annealing model (inspired by the cooling process of mextals). Such algorithms are used to solve many optimization problems in fields such as industry, business, engineering, etc. Another new algorithm presented in 2019 is the Harris hawk optimization algorithm, which is inspired by the hunting life of Harris hawks. Among the features of this method, we can mention the exact convergence of the optimal point by their collective movement method. But in this method, the rate of discovery and extraction is adjusted using the energy of the rabbit (E), which only depends on the number of evolution rounds of the algorithm, and in order to adjust it, the convergence value of Harris's hawks to the optimal point is not considered. In this thesis, we will improve this method in order to explore optimal inclusiveness with the aim of increasing accuracy and speed of convergence. For this purpose, a new mechanism is introduced to calculate the convergence value of Harris hawks, which is adjusted in the frxamework of a fuzzy inference system, rabbit energy. Also, in the proposed method, due to the preservation of diversity in the population and the improvement of exploration, a chaotic mapping is used, in which some Harris hawks explore the problem space by using logistic mapping. The simulation of the improved method of Harris Hawks algorithm has been tested on different standard test functions and the results show the improvement of the proposed algorithm in the accuracy and speed of convergence (number of function evaluations) compared to the basic method of Harris Hawks optimization.