Abstract
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. In order to accelerate the convergence speed of the bacterial colony near global optima, two cooperative approaches have been applied to BFO that resulted in a significant improvement in the performance of the original algorithm in terms of convergence speed, accuracy and robustness. The performance of the proposed cooperative variants are compared to the original BFO, the standard PSO, and a real-coded GA on a set of 4 widely-used benchmark functions, demonstrating their superiority.
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