Multi-objective bee colony optimization to tuning PID controller

In this study, a novel intelligent design method for closed-loop auto-tuning of a proportional-integral-derivative (PID) controller based on Bee Inspired Swarm Algorithms is proposed, in which PID controller parameters can be tuned concurrently. The set of trade-off optimal solutions, called Pareto-set optimization solutions, of the conflicting objective functions are able to be found. Moreover the research presents an investigation for the development of system identification using Artificial Bee Colony (ABC).Bees Algorithm (BA), developed by D.T. Pham (2006) and Artificial Bee Colony, developed by Dervis Karaboga (2005) are a subfield of Swarm Intelligence and was inspired by swarming patterns occurring in nature such as the food foraging behavior of honeybees. In the present study, the problem of identifying the PID controller parameters is considered as an optimization problem. The Multi-Objective Bees Algorithm (MOBA) and the Multi-Objective Artificial Bee Colony (MOABC) algorithm have been employed to determine the PID parameters. The PID controller is designed using the MOBA and the MOABC algorithms. The results of all designs are compared and analyzed. Simulation results demonstrate that the proposed method using the MOBA and the MOABC has a better control system performance. The results obtained show good stability, set-point tracking performance and robustness against disturbance.