Research On Evolutionary Equation And Other Algorithms Of Particle Swarm Optimization

The cross researches among different subjects offer new ideas to deal with optimization problems in modern manufacture. As per this idea, the new intelligent optimization algorithms based on biological intelligence or natural phenomenon are brought forward, which put up excellent performances during research and application. As a result, modern intelligent algorithm has become a new research focus of Artificial Intelligence. The work of this thesis mainly contains studying of improving methods of Particle swarm optimization and Artificial Immune Algorithm for unconstrained optimization problem.First, by analyzing evolutionary equation of particle swarm optimization algorithm a common evolutionary equation can be given. Then twelve equations as evolutionary equations of PSO algorithm are designed which contain the PSO by the way of turning altering of the coefficient base on the common equation. In addition, five equations are studied in this thesis. Numerical experiment shows these methods are feasible.Secondly, by studying of mutation strategy of AIA using real coding, a hybrid optimization method which takes evolutionary equation of PSO as the mutation strategy is proposed based on Fuzzy control AIA. Then a hybrid optimization algorithm with AIA and PSO could be designed.