DocumentCode :
2837279
Title :
Artificial Bee Colony Programming Made Faster
Author :
XingBao Liu ; Zixing Cai
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Volume :
4
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
154
Lastpage :
158
Abstract :
The artificial bee colony (ABC) algorithm is a stochastic, population-based evolutionary method that can be applied to a wide range of problems, including global optimization. The paper proposes a variation on the traditional ABC algorithm, called the artificial bee colony programming, or ABCP, employing randomized distribution, bit hyper-mutation and a novel crossover operator to significantly improve the performance of the original algorithm. Application of the new ABC algorithm on fifteen benchmark optimization problems shows a marked improvement in performance over the traditional ABC.
Keywords :
evolutionary computation; artificial bee colony programming; crossover operator; global optimization; population-based evolutionary method; randomized distribution; Artificial intelligence; Educational institutions; Educational programs; Educational technology; Genetic mutations; Information science; Optimization methods; Programming profession; Stochastic processes; Tin; artificial immune systems; clone selection algorithm; optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
Type :
conf
DOI :
10.1109/ICNC.2009.377
Filename :
5364518
Link To Document :
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