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Cited by 3,069

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Biomimicry of bacterial foraging for distributed optimization and control

·2002·IEEE Control Systems
DOI 10.1109/mcs.2002.1004010
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Abstract

We explain the biology and physics underlying the chemotactic (foraging) behavior of E. coli bacteria. We explain a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, we apply it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of biomimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, we provide some basic ideas and invite the reader to explore the concepts further.

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Cites

Show all 2 references

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Cited by

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Metadata

Venue
IEEE Control Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2002-06
Volume
22
Issue
3
Pages
52-67
ISSN
  • 1066-033X (ISSN-L)
  • 1941-000X
DOI
10.1109/mcs.2002.1004010
OpenAlex
W2122122715
Cited by
3,069
References
2
FWCI
14.72
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Biomimicry of bacterial foraging for distributed optimization and control. https://app.cochat.ai/papers/10.1109/mcs.2002.1004010
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Biomimicry of bacterial foraging for distributed optimization and control. (2002). IEEE Control Systems. https://doi.org/10.1109/mcs.2002.1004010

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.