Abstract
This paper proposes a novel bacterial colony foraging (BCF) algorithm for complex optimization problems. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The cell-to-cell communication enables the historical search experience sharing among the bacterial colony that can significantly improve convergence. With the self-adaptive strategy, each bacterium can be characterized by focused and deeper exploitation of the promising regions and wider exploration of other regions of the search space. In the experiments, the proposed algorithm is benchmarked against four state-of-the-art reference algorithms using a set of classical test functions.
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Liu, W., Zhu, Y., Niu, B., Chen, H. (2012). Optimization Based on Bacterial Colony Foraging. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_71
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DOI: https://doi.org/10.1007/978-3-642-31837-5_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31836-8
Online ISBN: 978-3-642-31837-5
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