Title :
The Adaptive Chemotactic Foraging with Differential Evolution algorithm
Author :
Jarraya, Yosr ; Bouaziz, Souhir ; Alimi, Adel M. ; Abraham, Ajith
Author_Institution :
Res. Group on Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia
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.
Keywords :
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; Convergence; adaptive computational chemotaxis; bacterial foraging; differential evolution; global optimization; hybrid algorithm;
Conference_Titel :
Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on
Conference_Location :
Fargo, ND
Print_ISBN :
978-1-4799-1414-2
DOI :
10.1109/NaBIC.2013.6617839