Improvement Of Crow Search Algorithm And Its Application In Engineering

Crow Search Algorithm(CSA)is a new swarm intelligence optimization algorithm proposed in recent years.Currently,CSA has been successfully applied in feature selection,image segmentation,task planning,and other aspects.However,crow search algorithm still has shortcomings such as slow global convergence speed and easy to fall into local optimization.To solve this problem,an improved crow search algorithm was developed and applied to solve the path planning problem of unmanned aerial vehicles.The content and research results of this thesis are as follows:(1)This thesis uses the method of combining real terrain data and manual modeling to establish the flight environment model of UAV.Since the path planning problem involves constraints such as path length,threat area,flight altitude,and path smoothing,corresponding cost functions are established for the constraints,and each item is weighted as the total fitness function.(2)Aiming at the shortcomings of Crow Search Algorithm(CSA),a Multi-mode Flying Crow Search Algorithm(MFCSA)is proposed.Based on the strength of foraging ability,the algorithm divides the population into two groups with strong foraging ability and weaker foraging ability.Those with strong foraging ability adopt the strategy of tailing and tracking the optimal target of the current group,and fly to the vicinity of the current optimal position of the group to carry out search activities under the guidance of group information,which enhances the local development ability of the algorithm;those with weak foraging ability adopt observation and learning The foraging method of the strong,and the two strategies of flying away quickly when encountering danger,the former can improve the global exploration ability of the algorithm,and the latter can maintain the diversity of the population.Through numerical experiments and engineering applications,it is verified that MFCSA has better performance in terms of optimization accuracy and convergence speed,enhances the ability to avoid falling into local optimum,and has better stability.(3)In order to solve the three-dimensional path planning problem of UAV,a multi-mode flying crow search algorithm based on spherical vector(SMFCSA)is proposed.The algorithm establishes a coordinate system in the sphere,which is related to the length of the flight path,turning angle,and climbing angle,and can reduce invalid search space.Simulation results show that SMFCSA can plan faster and more reliable flight paths.