Abstract

Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. The underlying biology behind the foraging strategy of Escherichia coli is emulated in an extraordinary manner and used as a simple optimization algorithm. The cross entropy function works well in case of bi-level thresholding problem. However, if there is a need of the multi-thresholding in image processing application, a global and generic objective function is desired so that each threshold could be tested for its best performance statistically. The maxima of the selected threshold is optimized by using the BFO algorithm based on constant chemo taxis length, constant rate of elimination and dispersion of bacteria and constant swim and tumbling of bacteria. The constant rate of swim, tumbling and rate of elimination and dispersion does not provide a natural optimization of the maxima of the threshold level from the given threshold levels.

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