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Binary-State Bacterial Foraging Optimization Based on Network Topology and its Application



Bacterial foraging optimization (BFO) inspired by the foraging behavior of E.coli has been used to solve optimization problems. This paper presents a novel binary-state bacterial foraging optimization based on network topology (BBFO-NT). In the proposed BBFO-NT, a binary-state bacterial foraging strategy, which makes the bacteria to have mutual learning mechanism, is introduced. The two behavioral states include an explorative state based on Von Neumann topology and an exploitative state based on small-world networks. The bacteria co-evolve during the optimization process under the two states. Experiments on a set of benchmark functions validate the effectiveness of the improved algorithm. BFO and some other intelligent optimization algorithms are employed for comparison. The simulations show that the proposed BBFO-NT offers significant improvements than BFO. On this basis, the new algorithm has been successfully applied to the docking control. The experiments indicate that the improved algorithm is effective in controller design.



Total Pages: 14
Pages: 271-284


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Volume: 23
Issue: 2
Year: 2016

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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