A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Automatic circle detection on images with an adaptive bacterial foraging algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Adaptive Computational Chemotaxis in Bacterial Foraging Algorithm
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Adaptive control of DC motor using bacterial foraging algorithm
Applied Soft Computing
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Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from forging behavior of E. coli. After analysis of optimization mechanism, a series of measures are taken to improve the classic BFO and we call it iBFO. In the modified method, both of search scope and chemotaxis step varies dynamically, which can markably accelerate the convergence and enhance the searching precision. Besides, a variable denoted the overall best value is incorporated to guide the bacterial swarm to move to the global optima and replace the role of interaction behavior between bacteria in classic BFO which is complicated and time-consuming. The superiority of the algorithm proposed is tested over several test functions and parameter estimation of NARMAX (nonlinear autoregressive moving average with exogenous) model. Simulated result shows that it has high efficiency, rapid speed of convergence and strong capability of global search.