Integration Of Particle Swarm Algorithm And Ant Colony Algorithm And Its Application

Particle swarm optimization and ant colony optimization are two classic swarm optimization algorithms.Since their introduction,they have been concerned by many scholars at home and abroad,and have been widely used in solving optimization problems in various fields.But both algorithms have their limitations and scope of applications.In this article,the method of integrating the two algorithms according to certain rules to make up for the shortcomings and give full play to their respective advantages are studied based on the analysis of the basic principles,algorithm flow and important parameters of particle swarm optimization and ant colony algorithm.The main works of this article is as follows:(1)Analyze the impact of inertial weight and learning factors on the performance of standard particle swarm optimization algorithm,and carry out experimental verification.Inertial weight and learning factors are the main parameters that affect the performance of particle swarm optimization.The experimental results show that the inertia weight mainly affects the convergence speed of the standard particle swarm optimization algorithm,and the decreasing inertia weight can significantly improve the convergence speed of the algorithm;the learning factors mainly affect the optimization accuracy of the algorithm,and an appropriate dynamic value strategy can improve the optimization probability of the algorithm and reduce the fluctuation range of the optimization.(2)Improve the particle swarm optimization algorithm,and theoretically analyze and verify the performance of the improved algorithm.In view of the shortcomings of particle swarm optimization in the late stage of particle swarm search,the optimization accuracy is not high.This paper proposes a particle swarm optimization algorithm based on cross mutation,by selecting the speed and position vector of half-particles with good fitness instead of the speed and position vector of half-particles with poor fitness,and keeping the individual extreme value of half-particles with poor fitness unchanged,cross each other in probability random variation.The experimental results show that when the inertia weights and learning factors are both optimal constants or the inertia weights are optimal constants,and the two learning factors are dynamically combined,the optimization accuracy of the algorithm is significantly improved.(3)Design an integrated algorithm based on the combination of particle swarm optimization and ant colony optimization.By using the advantages of the particle swarm optimization algorithm and the ant colony algorithm,an algorithm is proposed to integrate the two algorithms in series,and the classic TSP problem is used to verify the effectiveness of the integrated algorithm.In the integrated algorithm,First,the particle swarm algorithm with cross mutation operation is used to perform a rough search;then the iterative result is mapped to the initial pheromone matrix of the ant colony algorithm;and finally the ant colony algorithm is used for local search.The experimental results show that the integrated algorithm has a fast convergence speed,high optimization accuracy,and obvious optimization effect for large-scale problems.(4)The integrated algorithm based on particle swarm optimization and ant colony algorithm is applied to the path planning of mobile robots.The application methods are as follows: First,the grid method is used to model the known environment and each grid is encoded;then the particle swarm algorithm with cross-mutation operation is used for global path planning;and finally,let the robot moves along the planned path,if an obstacle is encountered,the ant colony algorithm is used for local path planning.after avoiding the obstacle,it returns to the previously planned path,so it is used alternately to find the optimal path.The simulation experiment results show that: the integrated algorithm can improve the convergence speed of the algorithm,shorten the path planning length,and the optimization effect is obvious when dealing with large-scale path planning problems.Comprehensive experiments show that the integrated algorithm is more superior in terms of convergence speed,and the optimization accuracy of the algorithm is also improved,which has good practicability.