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Reseach Article

Genetic Algorithm based Bacterial Foraging Approach for Optimization

Published on May 2012 by Nikhil Kushwaha, Vimal Singh Bisht, Gautam Shah
National Conference on Future Aspects of Artificial intelligence in Industrial Automation 2012
Foundation of Computer Science USA
NCFAAIIA - Number 2
May 2012
Authors: Nikhil Kushwaha, Vimal Singh Bisht, Gautam Shah
74c56ce6-285c-4e82-8d16-cd3a4e959008

Nikhil Kushwaha, Vimal Singh Bisht, Gautam Shah . Genetic Algorithm based Bacterial Foraging Approach for Optimization. National Conference on Future Aspects of Artificial intelligence in Industrial Automation 2012. NCFAAIIA, 2 (May 2012), 11-14.

@article{
author = { Nikhil Kushwaha, Vimal Singh Bisht, Gautam Shah },
title = { Genetic Algorithm based Bacterial Foraging Approach for Optimization },
journal = { National Conference on Future Aspects of Artificial intelligence in Industrial Automation 2012 },
issue_date = { May 2012 },
volume = { NCFAAIIA },
number = { 2 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 11-14 },
numpages = 4,
url = { /proceedings/ncfaaiia/number2/6733-1012/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Future Aspects of Artificial intelligence in Industrial Automation 2012
%A Nikhil Kushwaha
%A Vimal Singh Bisht
%A Gautam Shah
%T Genetic Algorithm based Bacterial Foraging Approach for Optimization
%J National Conference on Future Aspects of Artificial intelligence in Industrial Automation 2012
%@ 0975-8887
%V NCFAAIIA
%N 2
%P 11-14
%D 2012
%I International Journal of Computer Applications
Abstract

Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real world optimization problems arising in several application domains. The underlying biology behind the foraging strategy of E. coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This paper proposes a genetic algorithm (GA) based bacterial foraging (BF) algorithms for function optimization. The proposed method using test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Bacterial Foraging Technique Optimization