Harris Hawk Optimization Algorithm Based On Multi-population And Cooperative Quantizatio

In recent years,the engineering optimization problem has become increasingly complex.The traditional calculation methods can not effectively solve it,while the emergence of swarm intelligence algorithm has alleviated the problem to a certain extent.Harris hawks optimization(HHO)is a swarm intelligence algorithm proposed in 2019.The HHO algorithm has its own advantages,for example,this algorithm has strong optimization ability,can better jump out of local extremum,and has no cumbersome parameters to adjust.However,HHO algorithm still has some problems,such as insufficient balance between exploration and development abilities,which leads to slow convergence speed,low optimization accuracy and easy to fall into local optimization.Aiming at the problems of HHO algorithm,this these proposes three strategies to improve the basic HHO algorithm.The experimental results show that the improved HHO algorithm improves the performance of the algorithm.The initial population of Harris hawks optimization algorithm is limited,this paper introduces a multi-population strategy to improve it,and proposes a multi-energy strategy based on multi-population to simulate the escape process of preys with different physical abilities,so that the multiple populations can evolve in different directions.As a result,the search ability of the algorithm in the exploration and development stages is improved.In order to find the most efficient number of population division in this strategy,the improved strategy with the number of populations being 2,3,4,5,and the basic HHO algorithm are selected for experiments,and the optimal number is set to 2.In addition,a collaborative quantization strategy is proposed,which can effectively avoid the algorithm falling into local extremum in the early stage of iteration,and improve the optimization accuracy of the algorithm in the late stage of iteration.In order to verify the effectiveness of the collaborative quantization strategy,the HHO algorithm with the collaborative quantization strategy,are compared with the other five basic swarm intelligence algorithms.Finally,the multi-population multi-energy strategy and the collaborative quantization strategy,and proposes a Harris hawks optimization algorithm based on multi-population and collaborative quantization-MCQHHO algorithm.In order to verify the actual effect of the algorithm,this thesis selects 9 international benchmark functions,carries out numerical experiments with 5 basic swarm intelligence algorithms and 3 improved HHO algorithms.The experimental data are compared and analyzed.The results show that the MCQHHO algorithm greatly improves the convergence speed and optimization accuracy of the optimal solution,and has a stronger ability to jump out of the local optimum.