Cooperative Bacterial Foraging Optimization

Chen, Hanning; Zhu, Yunlong; Hu, Kun

doi:10.1155/2009/815247

Cited by 48 publications

(25 citation statements)

References 7 publications

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“…The original Bacterial Foraging Optimization system consists of three principal mechanisms, namely, chemo taxis, reproduction, and elimination-dispersal [19]. In the Chemotaxis process the movement of an E.coli cell through swimming and tumbling via flagellais simulated.…”

Section: Bacteria Foraging Algorithmmentioning

confidence: 99%

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan¹,

Venkatachalapathy²

2012

IJCA

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For the past few years, a number of new face recognition techniques have been proposed. Always it is a big unanswered question among face recognition researchers about which method or technique will have better performance. In this study an approach to recognize known faces based on Eigen vectors and a hybrid Meta-heuristic feature selection algorithm is proposed. The eigenvectors which are covariance matrix of the face images together describes the difference between face images. Face recognition problem is viewed as a two dimensional recognition problem. Initially the face images are projected in to face space and using Principal component analysis the eigenvectors with high Eigenvalues are extracted to reduce the dimension of the feature vector. Further to select the best feature vectors which increase the classification accuracy is selected by using a hybrid metaheuristic algorithm using Genetic algorithm (GA) and Bacteria Foraging Optimization (BFO). In this study the Support vector machine (SVM) and Back propagation neural network (BPNN) are used for classification. The classifiers are trained and tested separately using the frontal face images taken from AT&T database. The SVM and BPNN produces an average classification accuracy of 82.6% and 83.28% respectively.

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Section: Bacteria Foraging Algorithmmentioning

confidence: 99%

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

Arivalagan¹,

Venkatachalapathy²

2012

IJCA

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“…The motile bacteria such as E. coli and salmonella propel themselves by rotating their flagella. To move forward, the flagella counterclockwise rotate and the organism "swims" (or "runs").While a clockwise rotation of the flagellum causes the bacterium randomly "tumble" itself in a new direction and then swims again [24], [25].…”

Section: B Bacteria Foraging Optimizationmentioning

confidence: 99%

“…The original Bacterial Foraging Optimization system consists of three principal mechanisms, namely, chemo taxis, reproduction, and elimination-dispersal [25]:…”

Section: ) Classical Bfo Algorithmmentioning

confidence: 99%

Face Recognition Using Bacteria Foraging Optimization-Based Selected Features

Jakhar¹,

Kaur²,

Singh³

2011

SpecialIssue

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Abstract-Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. This paper presents a novel feature selection algorithm based on Bacteria Foraging Optimization (BFO). The algorithm is applied to coefficients extracted by discrete cosine transforms (DCT). Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). Performance is evaluated using the ORL face database.

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“…Three improved versions of BFO, namely the cooperative bacterial foraging optimizer (CBFO), adaptive bacterial optimization, and multicolony bacterial foraging optimization (MCBFO), have been described in our previous works [29,30,34]. In [29], instead of simply considering chemotaxis behavior of single bacterium, MCBFO also introduced the communication strategies employs by natural multi-colony bacterial community in order to coordinate pattern emerges, which is lacking from the mentioned chemotaxis inspired algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In [30], by employing the adaptive foraging strategies, two proposed ABFO algorithms are able to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. In [34], the proposed CBFO applied two cooperative approaches to the original BFO, namely the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level.…”

Section: Introductionmentioning

confidence: 99%