Abstract: Harris Hawk optimization(HHO) algorithm only uses the random strategy to initialize the population in the exploration stage,which decreases the population diversity.The escape energy that controls the linear variation of the development and exploration process is prone to fall into local optimum in the later stage of iteration.To address the issues,this paper proposes a binary Harris Hawk optimization for metaheuristic feature selection algorithm.First,in the exploration phase,the Sine mapping function is introduced to initialize the population location of Harris Hawk,and the adaptive adjustment operator is used to change the search range of HHO and update the population location of HHO.Second,the updated formula of escape energy is improved by the logarithmic inertia weight,the number of iterations are introduced into the jump distance,and the step size adjustment parameter is employed to adjust the search distance of HHO to balance the exploration and development capabilities.On this basis,an improved HHO algorithm is designed to avoid the HHO algorithm falling into the local optimum.Third,the binary position and population position of the improved HHO algorithm are updated by the S-type and V-type transfer functions.Thus two binary improved HHO algorithms are designed.Finally,a fitness function is used to evaluate the feature subset,the binary improved HHO algorithm is combined with this fitness function,and then two binary improved HHO metaheuristic feature selection algorithms are developed.Experimental results on 10 benchmark functions and 17 public datasets show that the four optimization strategies effectively improve the optimization performance of the HHO algorithms on these benchmark functions,and the improved HHO algorithm is significantly better than other compared optimization algorithms.On 12 UCI datasets and 5 high-dimensional gene datasets,when compared with the BHHO-based feature selection algorithms and the other feature selection algorithms,the results demonstrate that the V-shape-based improved HHO feature selection algorithm has great optimization ability and classification performance.

Key words: Feature selection, Metaheuristic, Binary, Harris Hawk optimization, Fitness function