Design of optimal disturbance rejection PID controllers using genetic algorithms

By Renato Antonio Krohling, J. Rey. IEEE Transactions on Evolutionary Computation, 2001

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A hybrid genetic algorithm and bacterial foraging approach for global optimization (2007, methodology): “The proposed algorithm is validated using four test functions and for PID controller tuning [18,29,17] in Section 3.”
Tuning and Assessment of Proportional–Integral–Derivative Controller for an Automatic Voltage Regulator System Employing Local Unimodal Sampling Algorithm (2014, methodology): “of PID controllers, such as Ziegler–Nichols tuning [2], the genetic algorithm (GA) [3], neural networks [4], fuzzy-based approaches [5], particle swarm optimization (PSO) [6], multiobjective optimization [7-8], differential evolution (DE) [9, 10], and the artificial bee colony (ABC) [11].”
Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems (2006, methodology): “More details on the GA used can be found in [46].”
Design of a fractional order PID controller for an AVR using particle swarm optimization (2009, background): “Although the ITSE performance criterion can overcome this drawback, but it cannot ensure to have a desirable stability margin (Krohling & Rey, 2001).”