Study on Mechanical Design Optimization Based on Improved ParticleSwarm Optimization Algorithm

摘要

In view of local optimization in particle swarm optimization algorithm (PSO algorithm), chaos theory wasintroduced to PSO algorithm in this paper. Plenty of populations were generated by using the ergodicity of chaotic motion.The uniformly distributed initial particles of the particle swarms were extracted from the populations according to theEuclidean distance between particles, so that the particles could uniformly distribute in the solution space. Local searchwas carried out on the optimal position of the particles during evolution, so as to improve the development capability ofPSO algorithm and prevent its prematurity, thus enhancing its global optimizing capability. Then the improved PSOalgorithm was applied to mechanical design optimization. With optimization design for two-stage gear reducer as thestudy object, objective function and constraint conditions were determined by building a mathematical model ofoptimization design, thus realizing optimization design. Simulation and comparison between the improved algorithm andunimproved algorithm show that improved PSO algorithm can optimize the optimization results of PSO algorithm at afaster convergence rate.