A Bacterial Foraging Optimization Algorithm with Self-Tuning

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Although thealgorithm has successfully been applied to many kinds of real word optimization problems, experimentation with complexproblems reports that the basic BFO algorithm possesses a poor performance. This paper presents a variation on the original BFO algorithm, called the Self-tuned Bacterial ForagingOptimization (STBFO), which employs a self tuning search strategy to significantly improve the performance of the originalalgorithm. This is because the STBFO adjusts the run-length unit parameter dynamically during evolution to keep a good balancebetween exploration and exploitation skills. Application of STBFO on several benchmark functions shows a marked improvement in performance over the original BFO.

Keywords:

optimization algorithms , bacterial foraging optimization (BFO) , self-tuned bacterial foraging optimization(STBFO)