Focused on the disadvantages of slow convergence and easy to fall into local optimum of Harris Hawks Optimization （HHO） algorithm， an improved HHO algorithm called Chemotaxis Correction HHO （CC-HHO） algorithm was proposed. Firstly， the state of convergence curve was identified by calculating the rate of decline and change weight of the optimal solution. Secondly， the CC mechanism of the Bacterial Foraging Optimization （BFO） algorithm was introduced into the local search stage to improve the accuracy of optimization. Thirdly， the law of energy consumption was integrated into the updating process of the escape energy factor and the jump distance to balance the exploration and exploitation. Fourthly， elite selection for different combinations of optimal solution and sub-optimal solution was used to improve the universality of global search of the algorithm. Finally， when the search was falling into local optimum， the escape energy was disturbed to realize the forced jumping out. The performance of the improved algorithm was tested by ten benchmark functions. The results show that the search accuracy of CC-HHO algorithm on unimodal functions is better than those of Gravitational Search Algorithm （GSA）， Particle Swarm Optimization （PSO） algorithm， Whale Optimization Algorithm （WOA） and other four improved HHO algorithms for more than ten orders of magnitude； there is also more than one order of magnitude superiority on multimodal functions； on the premise that search stability is improved by more than 10% on average， the proposed algorithm has faster convergence speed significantly than the above-mentioned several comparative optimization algorithms with more obvious convergence trend. Experimental results show that CC-HHO algorithm effectively improves the efficiency and robustness of the original algorithm.