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.
Bacterial Forging, Chemotaxis, Cooperative Foraging, Composition Function
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