Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis

Dasgupta, S.; Das, Saptarshi; Abraham, Ajith; Biswas, Arijit

doi:10.1109/tevc.2009.2021982

Cited by 227 publications

(116 citation statements)

References 29 publications

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“…The BFO algorithm was inspired by the complex organized activities in bacterial foraging and the survival of bacteria in different environments [8,106,107]. A BFO algorithm consists of several bacteria, which represent solutions in the optimization problem and consists of three processes: chemotaxis, reproduction, and elimination-dispersal.…”

Section: Bacterial Foraging Optimization (Bfo)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

439

195

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Bacterial Foraging Optimization (Bfo)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

439

195

View full text Add to dashboard Cite

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“…Besides its hybridization with other optimization systems, they provided an account of most of the significant applications of BFOA until 2009. In the same year, they evaluated, in [14], the chemotaxis operation in the algorithm. The undertaken study provides important insights into its search mechanism.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

2017

J Inf Process Syst

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Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

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“…The implementation of the Bacterial Foraging Optimization algorithm, which is a modern global optimization tool using iterative stochastic searches, is a computational analogue of the behaviour of intestinal bacteria in their search for nutrients in a hostile environment with minimal loss in energy. This technique, proposed by Kevin Passino in 2002, was inspired by the group foraging behaviour of Escherichia coli in the human intestine [55] . The BFO algorithm comprises four stages, namely: Chemotaxis, Swarming, Reproduction and Elimination-Dispersal.…”

Section: Bacterial Foraging Optimizationmentioning

confidence: 99%

Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting

Chakrabarty

Choudhury²,

Sarkar³

et al. 2012

AIR

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The present paper describes the development of a hyperspectral image classification scheme using support vector machines (SVM) with spectrally weighted kernels. The kernels are designed during the training phase of the SVM using optimal spectral weights estimated using the Bacterial Foraging Optimization (BFO) algorithm, a popular modern stochastic optimization algorithm. The optimized kernel functions are then in the SVM paradigm for bi-classification of pixels in hyperspectral images. The effectiveness of the proposed approach is demonstrated by implementing it on three widely used benchmark hyperspectral data sets, two of which were taken over agricultural sites at Indian Pines, Indiana, and Salinas Valley, California, by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) at NASA's Jet Propulsion Laboratory. The third dataset was acquired using the Reflective Optical System Imaging Spectrometer (ROSIS) over an urban scene at Pavia University, Italy to demonstrate the efficacy of the proposed approach in an urban scenario as well as with agricultural data. Classification errors for One-Against-One (OAO) and classification accuracies for One-Against-All (OAA) schemes were computed and compared to other methods developed in recent times. Finally, the use of the BFO-based technique is recommended owing to its superior performance, in comparison to other contemporary stochastic bio-inspired algorithms.

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Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis

Abstract: Abstract

Cited by 227 publications

References 29 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting

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