Improvement Of Particle Swarm Optimization And Ant Colony Algorithms

Particle swarm optimization(PSO)and ant colony algorithm(ACA)are hot topics of domestic and foreign scholars,which have been applied in various fields such as economy and engineering.Particle swarm optimization(PSO)and ant colony algorithm(ACA)are effective algorithms for solving complex optimization problems.However,their performance is insufficient.Therefore,In this paper,particle swarm optimization and ant colony algorithm are improved.The main results are as follows:(1)This paper systematically introduces the research background of particle swarm optimization(PSO)and ant colony algorithm(ACA)as well as the research status home and abroad.It briefly introduces PSO and ACA.It summarizes the shortcomings of the two algorithms and proposes corresponding improvement methods.(2)Elite strategy and adaptive dynamic Levy flight step are introduced into particle swarm optimization,and a new algorithm(ELPSO)is proposed.Six standard test functions are used to test the improved algorithm(ELPSO).Compared with the standard PSO algorithm and the weight linear decreasing particle swarm optimization(RWPSO).The results show that ELPSO algorithm has remarkable accuracy and convergence speed.It is improved and applied to solve the problem of logistics location.(3)Tent chaotic mapping is introduced into the basic discrete particle swarm optimization algorithm.Meanwhile,3-opt local search algorithm is added to the algorithm,and an improved discrete particle swarm optimization algorithm is proposed to solve the TSP problem.The results show that the convergence speed and accuracy of the algorithm are better than the basic ant colony optimization algorithm.(4)Simulated annealing mechanism and adaptive chaotic disturbance are applied to ant colony algorithm,and a new pheromone update operator is used.The improved ant colony algorithm is applied to traveling salesman problem(TSP)and three-dimensional path planning problems.The results show that the improved ant colony algorithm can effectively solve TSP and three-dimensional path planning problems.