Paper Info
Title
The Adaptive Chemotactic Foraging with Differential Evolution algorithm
Abstract
This work proposes the application of a novel evolutionary approach called the Adaptive Chemotactic Foraging with Differential Evolution algorithm (ACF_DE) on benchmark problems. This method is based on the well-known Bacterial Foraging Optimization Algorithm (BFOA), applying appropriate Differential Evolution operators and including an adaptation scheme of the chemotaxis step size to concentrate the search in the desired optimal zone. The hybrid system is compared with those of related methods on benchmark problems showing its high performance in overcoming slow and premature convergence.
Year
DOI
Venue
2013
10.1109/NaBIC.2013.6617839
Nature and Biologically Inspired Computing
Keywords
Field
DocType
convergence,evolutionary computation,swarm intelligence,ACF_DE,BFOA,adaptation scheme,adaptive chemotactic foraging,bacterial foraging optimization algorithm,chemotaxis step size,differential evolution algorithm,differential evolution operators,evolutionary approach,hybrid system,optimal zone,premature convergence,swarm intelligence,adaptive computational chemotaxis,bacterial foraging,differential evolution,global optimization,hybrid algorithm
Convergence (routing),Mathematical optimization,Premature convergence,Computer science,Swarm intelligence,Evolutionary computation,Differential evolution,Evolution strategy,Artificial intelligence,Hybrid system,Foraging,Machine learning
Conference
ISSN
ISBN
Citations 
2164-7364
978-1-4799-1414-2
4
PageRank 
References 
Authors
0.40
11
4
Name
Order
Citations
PageRank
Yosra Jarraya140.40
Souhir Bouaziz2110.84
Adel M. Alimi3818.88
Ajith Abraham48954729.23