The bacterial colony optimization algorithm is an optimization algorithm which is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration.[1] The bacterial foraging algorithm (BFA) is a biologically inspired swarm intelligence optimization approach that mimics bacteria's foraging activity to gather the most energy available throughout the search phase. Since its introduction in 2002, it has garnered widespread interest from scholars. [2]
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What is the Ant Colony Optimization Algorithm?
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IEE/CSE 598: Lecture 6B (2020-03-16) - Bacterial Foraging Opt./Intro to Particle Swarm Optimization
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Bacteriol Foraging Optimization Part 1
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References
- ^ Niu, Ben; Wang, Hong (2012). "Bacterial Colony Optimization" (PDF). Discrete Dynamics in Nature and Society. 2012: 1–28. doi:10.1155/2012/698057.
- ^ Pang, Shinsiong; Chen, Mu-Chen (June 1, 2023). "Optimize railway crew scheduling by using modified bacterial foraging algorithm". Computers & Industrial Engineering. 180: 109218. doi:10.1016/j.cie.2023.109218. ISSN 0360-8352. S2CID 257990456.
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