Research On Improved Colony Algorithm And Its Parametric Optimization For PID

Bee Colony Optimization which is also called standard bee colony optimization is a heuristic algorithm of swarm intelligence for solving global optimization problem. The basic ideas come from simulating bees'behavior. People found bee colony optimization not only can be used to solve simple and nonlinear function, but also can be used to optimize combinatorial problems by a great deal of scientific research and engineering practice. Bee colony optimization's defect that is easy to fall into the local minimum and slow convergence speed is not neglected, although this algorithm is simple and easy to implement. The researchers improve bee colony optimization to increase practicability and expand application fields.First, this paper systematically had analyzed bee colony optimization. It with genetic algorithm, particle swarm algorithm compare results that they optimize PID parameters.Secondly, this paper analyzes the improved bee colony optimization has occurred by, according to the improvement principle. The improved algorithm includes artificial bee colony algorithm based on Boltzmann selection policy, artificial bee colony algorithm with tabu strategy, Self--adapting search space chaos-artificial bee colony algorithm, Improved artificial bee colony algorithm based on genetic hybrid genes and so on. On the basis of predecessors, this paper puts forward Artificial Immune Bee Colony Optimization (AIBCO). This algorithm introduces antibody breeding strategies and antibody concentration regulation mechanism In the process of evolution, it dynamically adjusts the diversity of population and the algorithm convergence speed, to avoid into the local minimum value. Optimizing four standard functions to test performance of the algorithm.Finally, the paper specific describes artificial immune bee colony optimization of steps how to optimize the setting PID parameters. Through the MATLAB simulation results prove this algorithm is feasibility and availability in PID parameters optimization aspects.