Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning

Authors

Xiaojian Yang, College of Electronics and Information Engineering, Nanjing Tech University, Nanjing 211816, China;
Yiwei Dong, College of Electronics and Information Engineering, Nanjing Tech University, Nanjing 211816, China;

Abstract

Abstract: On the basis of analyzing such shortcomings of the artificial bee colony algorithm (ABC) as slow convergence, low convergence precision and premature convergence, the opposition-learning adaptive quick artificial bee colony algorithm (OAQABC) was proposed. A new step size was proposed, which made the around food source parameter of quick artificial bee colony algorithm (QABC) adaptive, and combined the opposition-based learning to improve the employed bee phase. The experimental results show that OAQABC has better performance than basic ABC and QABC. Also the optimization performance of OAQABC is better than particle swarm optimization (PSO) algorithm and Cuckoo Search (CS) algorithm obviously in the experiment.

Recommended Citation

Yang, Xiaojian and Dong, Yiwei (2020) "Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning," Journal of System Simulation: Vol. 28: Iss. 11, Article 6.
DOI: 10.16182/j.issn1004731x.joss.201611006
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss11/6

First Page

2684

Revised Date

2015-05-11

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201611006

Last Page

2692

CLC

TP301.6

Recommended Citation

Yang Xiaojian, Dong Yiwei. Adaptive Quick Artificial Bee Colony Algorithm Based on Opposition Learning[J]. Journal of System Simulation, 2016, 28(11): 2684-2692.

DOI

10.16182/j.issn1004731x.joss.201611006