A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation

By Sukumar Mishra. IEEE Transactions on Evolutionary Computation, 2005

492 citations recorded by the source corpus.

Subfields: Computational Mechanics, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/45f077ecc1e0eb1b8a4cbfc435a618397fc0bcbb

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In Computational Mechanics

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Citations in the corpus

New inspirations in swarm intelligence: a survey (2011, methodology): “…used for some applications such as multivariate PID controller tuning (Kim and Cho, 2005a; Luo and Chen, 2010), power systems harmonic estimation (Mishra, 2005), power transmission loss optimisation (Tripathy et al., 2006), machine learning (Kim and Cho, 2005b), multi-objective optimisation…”
IMPROVED ADAPTIVE BACTERIA FORAGING ALGORITHM IN OPTIMIZATION OF ANTENNA ARRAY FOR FASTER CONVERGENCE (2008, unknown): “The E. coli bacteria that are present in our intestines have a foraging strategy governed by four processes, namely, chemotaxis, swarming, reproduction, and elimination and dispersal [ 25 ].”
Bacteria Foraging-Based Solution to Optimize Both Real Power Loss and Voltage Stability Limit (2007, methodology): “BFA has been recently proposed [12] and further applied for harmonic estimation problem in power systems [13].”
Bacteria foraging based solution to optimize both real power loss and voltage stability limit (2007, methodology): “Fig. 5. Reproduction schemes proposed (Min) versus [ 13 ] (average).”
Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation (2007, methodology): “In this paper, the BF algorithm suggested in [9] and [ 10 ] is modified so as to expedite the convergence.”
Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications (2009, methodology): “Hence FBF, as presented in [ 29 ], may not be suitable for optimizing any benchmark function in general.”
Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems (2013, methodology): “This optimization procedure comprises of four basic steps: Swarming and tumbeling Chemotaxis, Reproduction and Elimination and Dispersal [22, 23, 26].”