Harris Hawks Optimization Algorithm And Its Application Research

Harris Hawks Optimizer(HHO)is a swarm intelligent optimization algorithm inspired by the cooperation and pursuit behavior between Harris hawk prey groups in nature.The Harris Hawks optimization algorithm has been noted for its benefits,such as having minimal parameters,straightforward structure,and powerful search capabilities.Nonetheless,scholars have discovered that the algorithm is burdened with certain shortcomings,such as irregularities in global and local search abilities which leads to local optimization.As a result,this research offers two alternative algorithms grounded on the main Harris Hawks optimization approach.Consequently,these improved algorithms were utilized in tackling practical optimization issues to enhance the theoretical foundation of the HHO method and broaden its applications.The following are the main research works presented in this paper:(1)To solve the discrete optimization problem and improve the optimization speed and accuracy of the algorithm,a discrete Harris Hawks Optimizer(DHHO)is proposed.DHHO is applied to the scheduling problem of shared electric vehicles with charging piles,and eight discrete functions are compared.The experimental results show that the discrete functions have different performances in the scheduling problem of shared electric vehicles with charging piles under different dimensions.(2)In order to enhance the exploration and development ability of HHO,improve the accuracy of the algorithm,expand the diversity of the population,and increase the horizontal crossing,vertical crossing,and elite reverse learning mechanism,an enhanced Harris Hawks Optimizer(EHHO)is proposed.Comparing EHHO with other algorithms,the experimental results show that EHHO has strong exploration and development capabilities on the standard function set,and is significantly superior to other algorithms.(3)The Harris Hawk optimization algorithm based on a spherical vector is proposed and applied to the path planning of safe UAVs.The search path is coded into a series of UAV flight path sequences so that the path evolves in the process of individual generation in EHHO.The experimental test compares the results of EHHO based on the spherical vector to solve the UAV path planning problem with the other six heuristic optimization algorithms.The results of the experiment indicate that EHHO outperforms its algorithm,providing further evidence that EHHO is a viable and efficient algorithm.