Improvement Appand Application Of Particle Swarm Optimization Algorithm

Particle swarm optimization(PSO)algorithm is a new stochastic optimization algorithm.Since it was proposed,it has been highly valued by scholars at home and abroad,and has been widely used in varnous fields of scientific research and practical engineering.However,so far,PSO algorithm has not been fully matured in theoretical research and practical application,and there are still a lot of problems to be studied.In this paper,the improvement of PSO algorithm and some applications is studied.The specific work is as follows:(1)Aiming at the shortcomings of PSO algorithm which is easy to fall into local extremum in the search process,a self-adaptive chaotic particle swarm optimization algorithm(SACPSO)is proposed.In the process of searching,the algorithm improves the ability of global exploration and local exploitation by adjusting the value of parameters adaptively,and judges whether the population falls into local extremum by calculating the variance of population fitness.When the population falls into the local extremum,the proposed adaptive Logistic chaotic map is used to optimize the global optimal particle and guide the population to jump out of the local extremum.The experimental results of the test function show that SACPSO algorithm has good optimization performance and stability,and can effectively avoid falling into local extremum.(2)The proposed SACPSO algorithm is applied to path planning in three dimensional space.The simulation results show that SACPSO algorithm can further improve the quality and efficiency of PSO algorithm in generating path solutions in three-dimensional space.It provides an effective method for path planning in three dimensional space.(3)Aiming at the shortcomings of PSO algorithm in solving traveling salesman problem.such as slow convergence speed and the accuracy of the solution is not high,a chaotic particle swarm optimization algorithm(ULCPSO)based on unequal probability operative factor and local search is proposed.In this algorithm,the concept of unequal probability operative factor is proposed,which assigns an unequal probability value to each edge to improve the probability of selecting a shorter edge.In the search process,a local search strategy is added for improving the local search ability of the algorithm.Logistic chaotic maps are added to the iterative formula of the algorithm to improve the randomness and diversity of the particles and enhance the global search ability of the algorithm.Simulation experiments show that the ULCPSO algorithm has faster convergence speed and higher accuracy when solving traveling salesman problem.