使用多策略改进的哈里斯鹰优化算法OA

In view of the problems that the Harris hawks optimization is prone to fall into local optimum and has low optimization accuracy,a multi-strategy improved Harris hawks optimization is proposed.Based on the clas-sic Harris hawks optimization,the circle chaotic mapping is utilized to initialize the population,thereby enhancing the diversity of the initial population.A nonlinear energy factor update method is adopted to better balance the de-velopment and exploration process of the proposed algorithm.The Cauchy variation weight is set as a random vari-able within a specific interval,which decreases as the number of iterations increases,thereby enhancing the popula-tion's exploration ability in the early stage and search ability in the later stage.A learning strategy based on orthogo-nal design is introduced to generate new individuals as learning objects,and an individual is randomly selected to execute this strategy before the end of each iteration,thereby further enhancing the optimization ability of the pro-posed algorithm.The simulation experiment results show that the proposed improved algorithm has higher optimiza-tion accuracy and faster optimization speed,which is significantly better than other comparison algorithms and has a good application prospect in practical engineering problems.