DocumentCode :
2774706
Title :
Cooperative Bacterial Foraging algorithm for global Optimization
Author :
Chen, HanNing ; Zhu, Yunlong ; Hu, KunYuan
Author_Institution :
Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang, China
fYear :
2009
fDate :
17-19 June 2009
Firstpage :
3896
Lastpage :
3901
Abstract :
Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. In order to accelerate the convergence speed of the bacterial colony near global optima, two cooperative approaches have been applied to BFO that resulted in a significant improvement in the performance of the original algorithm in terms of convergence speed, accuracy and robustness. The performance of the proposed cooperative variants are compared to the original BFO, the standard PSO, and a real-coded GA on a set of 4 widely-used benchmark functions, demonstrating their superiority.
Keywords :
genetic algorithms; microorganisms; particle swarm optimisation; E. coli bacteria; bacterial foraging optimization; cooperative bacterial foraging algorithm; global optimization; nature-inspired algorithms; social foraging behavior; Acceleration; Automation; Computational modeling; Computer applications; Convergence; Machine learning algorithms; Microorganisms; Optimal control; Robustness; Space exploration; Bacterial Foraging Optimization; Cooperative BFO; GA; PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
Type :
conf
DOI :
10.1109/CCDC.2009.5191509
Filename :
5191509
Link To Document :
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