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This paper proposes a smart Bacterial Foraging optimization Algorithm an intelligent exploitation of biologically inspired group foraging behavior of Escherichia coli (E-Coli) present in the human intestine. Intelligence is a key feature in the next generation of Grid computing and is embedded in the authors' Bacterial Foraging optimization algorithm as a core component for scheduling efficiently and effectively. The intelligence is incorporated to the existing BFO by incorporating Tabu search in the Chemotaxis, augmenting Genetic Algorithm to the reproduction phase and applying Simulated Annealing to the elimination dispersal loop. Their experimental results on various benchmark instances provide strong evidence that shows schedules with smaller makespan and higher robustness compared with other existing approaches gratuitous to the new enhancement.
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