DocumentCode :
238697
Title :
A novel Artificial Bee Colony algorithm with integration of extremal optimization for numerical optimization problems
Author :
Min-Rong Chen ; Guo-Qiang Zeng ; Wei Zeng ; Xia Li ; Jian-Ping Luo
Author_Institution :
Sch. of Comput. Sci., South China Normal Univ., Guangzhou, China
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
242
Lastpage :
249
Abstract :
Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. The standard ABC is weak at the local-search capability and precision. Extremal Optimization (EO) is a general-purpose heuristic method which has strong local-search capability and has been successfully applied to a wide variety of hard optimization problems. In order to strengthen the local-search capability of ABC, this work proposes a novel hybrid optimization method, called ABC-EO algorithm, through introducing EO to ABC. The simulation results show that the performance of the proposed method is as good as or superior to those of the state-of-the-art algorithms in complex numerical optimization problems.
Keywords :
heuristic programming; optimisation; search problems; ABC-EO algorithm; artificial bee colony algorithm; extremal optimization; general-purpose heuristic method; hard optimization problems; hybrid optimization method; intelligent honeybee swarm behaviour; local-search capability; numerical optimization problems; Accuracy; Convergence; Educational institutions; Optimization; Simulation; Sociology; Standards; Artificial Bee Colony; Extremal Optimization; numerical optimization problems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900283
Filename :
6900283
Link To Document :
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