Computational Foraging in Bacterial Colony over Composition Environments

The social foraging behavior of E. coli bacteria has been used to solve optimization problems. This work proposes a novel cooperative bacterial foraging algorithm (CBFA) for complex optimization problems. The proposed CBFA extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and an adaptive foraging mechanism. Then the performance analysis is given where the proposed algorithm is benchmarked against four state-of-the-art reference algorithms using a composition test function suites. Statistical analysis result highlights the significant performance improvement due to the beneficial combination and shows that the proposed algorithm outperforms the reference algorithms