Study On Optimization Of HSIC Parameter Based On Improved Particle Swarm Optimization

Human-simulated intelligent control show it's unique advance in solving complex system , how One of main problems when applying human-simulated intelligent control is parameters optimization of controllers. With the increase of complexity of systems, the structure of human-simulated intelligent controller becomes also more and more complex, therefore there is much difficulty for some experts in the parameters optimization. Hereby, the paper developed a method resolving the parameters optimization of human-simulated intelligent controller based on evolution thought.Particle Swarm Optimization (PSO), have been proposed to solve this problem. As a relatively new evolutionary computation, PSO algorithm applies the information communication within particles to update itself, instead of any genetic operator such as crossover and mutation. All the particles are able to adjust their velocity and remember the best position in the past. This optimization algorithm has been applied to many engineering problem successfully.PSO is an efficient stochastic global optimization technique. PSO algorithm will stuck at local optima easily. Aiming at disadvantage of classical PSO algorithm , this paper puts forward some new improved algorithm and prove that improve PSO is effective and feasible.In the research process, the following works have been completed:①Synthesis the history and the current research progress of Computational Intelligence, PSO。②Summarize the principles of PSO and put forward some improved PSO③Research on HSIC and choose one of improved PSO to optimize HSIC parameter。Practices demonstrate the effectiveness of proposed methods resolving the parameters optimization problems of human-simulated intelligent controller based on evolution thought.