Biomimicry of bacterial foraging for distributed optimization and control

By Kevin M. Passino. 2002

3,206 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Modeling and Simulation, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/44cef1762d8091b69bb0ae14d7a764ff55e5304b

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In Biomedical Engineering

In Modeling and Simulation

In Molecular Biology

Citations in the corpus

Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, background): “…Algorithms: There are many other nature-inspired algorithms, such as simulated annealing (Kirkpatrick et al., 1983), bacteria foraging optimization [Passino(2002)], biogeography-based optimization [Simon(2008)], gravitational search (Rashedi et al.,2009), charged particle system [Kaveh and…”
Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation (2007, methodology): “This new optimization technique was proposed by Passino [9] and further incorporated by Mishra [10] foroptimization.”
A survey on new generation metaheuristic algorithms (2019, background): “The four main components of BFOA are; Chemotaxis: simulates the movement of an E.coli by swimming and tumbling using flagella.”
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications (2015, background): “[8] and Passino [9] proposed the bacterial foraging optimization (BFO), inspired by the group foraging behavior of bacteria such as E.”
Stability analysis of social foraging swarms (2004, methodology): “In [23], where social foraging was considered as an optimization process, a profile of this type was considered and con-”
IMPROVED ADAPTIVE BACTERIA FORAGING ALGORITHM IN OPTIMIZATION OF ANTENNA ARRAY FOR FASTER CONVERGENCE (2008, positive): “BFA is based on the foraging behavior of Escherichia Coli (E. Coli) bacteria present in the human intestine [23] and already been in use to many engineering problems including antenna arrays [24–30].”
New inspirations in swarm intelligence: a survey (2011, methodology): “In recent years, new swarm intelligence algorithms have appeared, inspired by bacterial foraging (BFO) (Passino, 2002), fireflies bioluminescense (Krishnanand and Ghose, 2009; Yang, 2008), slime moulds life cycle (Monismith and Mayfield, 2008), cockroaches infestation (Havens et al.”
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications (2009, methodology): “Bacteria Foraging Optimization Algorithm (BFOA), proposed by Passino [1], is a new comer to the family of nature-inspired optimization algorithms.”
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey (2011, background): “line and random movements to reach nutrient-rich locations [7].”
Multirobot systems: a classification focused on coordination (2004, methodology): “Another biologically inspired model, based on the behavior of E. coli bacteria, is presented in [88], where a cooperative foraging task is addressed.”
Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems (2013, methodology): “4.1 Bacteria foraging optimization algorithm Page 8 of 37 Ac ce pt ed M an us cr ip t 8 Bacteria Foraging Optimization Algorithm (BFOA), proposed by Passino [22], is a new comer to the family of nature-inspired optimization algorithms.”
A review on weight initialization strategies for neural networks (2021, methodology): “Cho et al. (2007) have proposed bacterial foraging algorithm (Passino 2002) for finding optimal input weights for ELM.”
Adaptation, Learning, and Optimization over Networks (2014, background): “They are also well-suited to model several forms of complex behavior exhibited by biological [16, 50, 131, 147] and social networks [15, 77, 92, 121, 230] such as fish schooling [188], prey-predator maneuvers [105, 171], bird formations [110, 119], bee swarming [12, 22, 125, 220], bacteria motility ”