Improvement And Application Study Of Harris Hawk Algorithm

Harris hawks algorithm is a new type of nature-inspired optimization algorithm.This algorithm simulates the process of hunting and trapping prey by Harris hawks,and has characteristics such as few parameters,simple structure,fast convergence speed,and strong local search capability.However,the Harris hawk algorithm has defects in model building,and it is easy to get trapped in local optima when dealing with non-convex function optimization problems.To address these issues,this paper proposes two improved Harris hawk algorithms and applies them to function extreme value optimization and engineering design optimization problems,expanding the application field of the Harris’ hawk algorithm.The main work of this paper includes:(1)Harris hawks algorithm based on adaptive mutation and Gaussian driving is proposed.First,during the exploration stage,a more reasonable selection mechanism is used to determine the Harris hawk’s next position update method.Secondly,in the transition from the exploration stage to the development stage,random numbers conforming to the Gaussian distribution are used instead of energy initialization random numbers,which can ensure more exploration stages in the later stages of the algorithm and improve the global search capability of the algorithm.Finally,an adaptive mutation strategy is proposed,which selects Gaussian mutation or uniform mutation based on energy values,so as to better help the algorithm jump out of local optima.The stability and convergence of the algorithm are verified through 23 benchmark test functions,and the improved algorithm is used to solve the pressure vessel design problem,obtaining a good design solution.(2)Harris hawks algorithm based on Brownian motion mutation strategy is proposed.Firstly,using the randomness of Brownian motion,a novel mutation strategy is constructed,which does not require input parameters or rely on location information among populations,and can better help the algorithm jump out of local optima.Secondly,the Harris hawks algorithm is combined with the Brownian motion mutation strategy to solve non-convex function optimization problems,and the improved Harris hawks algorithm(HHOBM)is proposed.Finally,the effectiveness of the HHOBM algorithm is demonstrated by ten CEC 2019 benchmark test functions,and its universality and effectiveness are verified by applying it to the cantilever beam design problem.