Tuning of optimal classical and fractional order PID parameters forautomatic generation control based on the bacterial swarm optimization
Abstract Particle Swarm Optimization algorithm converges rapidly during the initial stage of a
global search, but around global optimum, the search process slows down. In order to
overcome this problem and to further enhance the performance of Particle Swarm
Optimization, this paper implements a hybrid algorithm, Bacterial Swarm Optimization,
combining the features of Bacterial Foraging Optimization and Particle Swarm Optimization.
The PID parameters of classical and fractional-order controllers are optimized with Bacterial …
global search, but around global optimum, the search process slows down. In order to
overcome this problem and to further enhance the performance of Particle Swarm
Optimization, this paper implements a hybrid algorithm, Bacterial Swarm Optimization,
combining the features of Bacterial Foraging Optimization and Particle Swarm Optimization.
The PID parameters of classical and fractional-order controllers are optimized with Bacterial …
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