Employing Artificial Bee Colony with dynamic inertia weight for optimal tuning of PID controller

Artificial Bee Colony (ABC) is one of the most recently evolutionary computation algorithms. Artificial Bee Colony has limitations due to its stochastic searching characteristic and complex computation that result in slow convergence to the global optimum solution. In this paper, an efficient dynamic inertia weight based method is introduced to improve the performance of the standard ABC algorithm by controlling the effect of the initial population in the new expected solution which leads improvment in the convergence rate of ABC. The performance of the modified algorithm is compared with the standard ABC in the tuning of (PID) controller.The considered plants have different orders and time delays are controlled by PID controller with optimum gains. Simulation results show that the dynamic inertia weight ABC achieve faster convergence rate and optimum PID parameters.