Bacterial Foraging Optimization (BFO) algorithm is widely adopted to solve a variety
of engineering optimization tasks. In this paper, the Brownian Distribution (BD) strategy
guided BFO algorithm is proposed. During the optimization exploration, BD monitors
and controls the chemotaxis operation of the BFO algorithm inorder to enhance the
search speed and optimization accuracy. In the proposed algorithm, after undergoing
a chemotaxis step, each bacterium gets mutated by a BD operator. In the proposed work,
this algorithm is employed to design the PID controller for an AVR system and unstable
reactor models. The success of the proposed method has been confirmed through a comparative
analysis with PSO, BFO, adaptive BFO and PSO + BFO based hybrid methods existing in
the literature. The result shows that, for unstable reactor models, the BD guided
BFO algorithm provides better optimization accuracy compared to other algorithms considered
in this study.
