Performance Analysis Of Harris Hawks Optimization Algorithm And Its Application In Engineering

Harris Hawks Optimization(HHO)is a new intelligent optimization algorithm developed in the background of Harris hawks chasing prey.The HHO algorithm has the characteristics of simple structure,few parameters,fast convergence speed and high solution accuracy.Therefore,it is widely used in image processing,engineering optimization,pattern recognition,combinatorial optimization and other engineering fields.However,in the process of research,it was found that the algorithm also had shortcomings.For example,the exploration ability of the algorithm in the early stage is not strong,which affects the convergence speed,and in the later stage of iteration,the solution performance of the algorithm is reduced due to the decrease of population diversity.In view of the above problems,this paper proposes three improved HHO algorithms based on their performance analysis,and applies the improved algorithms to engineering optimization,chemical dynamic optimization,and nonlinear equation system problems,in order to expand the application scope of the algorithm.This paper focuses on the following aspects:(1)In order to enhance the exploration capability of the algorithm,a cubic chaos operator is introduced.Introduce cross-cutting strategies in the exploitation phase to increase population diversity.An improved cubic chaotic harris hawks optimization algorithm(CCHHO)is proposed.By applying the CCHHO algorithm to 17 benchmark functions and 3 engineering optimization problems,through comparative analysis,the convergence speed of the improved algorithm is improved,the algorithm has the ability to jump out of the local optimal,and the solution performance is stronger.(2)In order to balance the exploration and development capabilities of the HHO algorithm,a nonlinear escape energy factor is proposed.In the hard siege and soft siege strategies in the development stage,in order to make full use of the optimal position of individuals between populations,an elite individual-guided renewal strategy is proposed.A dynamic elite harris hawks optimization algorithm(DEHHO)is proposed.The improved algorithm is applied to five classic chemical dynamic optimization problems,and compared with the current mainstream intelligent optimization algorithm.Studies have shown that DEHHO is an efficient and competitive algorithm.(3)In the exploration stage,in order to improve the solution speed of the algorithm,a quadratic interpolation operator is introduced.In the exploitation stage,according to the precocious mechanism,in order to prevent the algorithm from falling into local optimum,a differential evolutionary variation operator is introduced.A Harris hawks optimization algorithm based on quadratic interpolation(QIHHO)is proposed.The application of QIHHO to the solution of five nonlinear equation systems and geometric constraint problems has higher solving accuracy and search ability than other algorithms.