Bacterial colony foraging algorithm: Combining chemotaxis, cell-to-cell communication, and self-adaptive strategy

Inspired by the colony foraging behavior of Escherichia coli bacteria, this paper proposes a novel bacterial colony foraging optimization (BCFO) algorithm for complex optimization problems. The main idea of BCFO is to develop an adaptive and cooperative life-cycle model by combining bacterial chemotaxis, cell-to-cell communication, and self-adaptive searching strategies. The proposed BCFO is a more bacterially-realistic model that the bacteria split and die dynamically throughout the foraging process and the population size varies as the algorithm runs. The cell-to-cell communication enables the bacteria tumble towards better directions in the chemotactic steps to speed up convergence. With the self-adaptive searching strategy, each bacterium can maintain an appropriate balance between exploration and exploitation. Seven versions of BCFO combined by different strategies under the model were proposed and tested on both static and dynamic benchmarks. Then the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these tests highlights the significant performance improvement due to the beneficial combination and shows that the proposed algorithm outperforms the reference algorithms.