A Hybrid Artificial Bee Colony algorithm for numerical function optimization

Author

Yan, Xiaohui ; Zhu, Yunlong ; Zou, Wenping

Author_Institution

Key Lab. of Ind. Inf., Shenyang Inst. of Autom., Shenyang, China

fYear

2011

fDate

5-8 Dec. 2011

Firstpage

127

Lastpage

132

Abstract

Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence algorithms which used for problem optimization. This paper presents a Hybrid Artificial Bee Colony algorithm (HABC), in which the crossover operator of Genetic Algorithm is introduced, to improve the canonical ABC in solving complex optimization problems. The variation of the algorithm is presented and seven benchmark functions are used to check its validity. The simulation results showed that the proposed HABC outperforms the canonical ABC and Particle Swarm Optimization algorithms on most functions, especially on the multimodal functions.

Keywords

genetic algorithms; particle swarm optimisation; ABC algorithm; HABC; benchmark functions; canonical ABC; complex optimization problems; crossover operator; genetic algorithm; hybrid artificial bee colony algorithm; multimodal functions; numerical function optimization; particle swarm optimization algorithms; problem optimization; swarm intelligence algorithms; Benchmark testing; Genetic algorithms; Hybrid intelligent systems; Optimization; Particle swarm optimization; Probability; Radiation detectors; Artificial Bee Colony; Crossover; Hybrid Artificial Bee Colony; Particle Swarm Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2011 11th International Conference on

Conference_Location

Melacca

Print_ISBN

978-1-4577-2151-9

Type

conf

DOI

10.1109/HIS.2011.6122092

Filename

6122092