A novel DE-ABC-Based hybrid algorithm for global optimization

Authors:
Li Li;Fangmin Yao;Lijing Tan;Ben Niu;Jun Xu
Affiliations:
College of Management, Shenzhen University, Shenzhen, China;College of Management, Shenzhen University, Shenzhen, China;Management School, Jinan University, Guangzhou, China;College of Management, Shenzhen University, Shenzhen, China;e-Business Technology Institute, The University of Hongkong, Hongkong, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

A novel hybrid swarm intelligent algorithm DEABC, integrating differential evolution (DE) and artificial bee colony (ABC) algorithm, is proposed in this paper. By using global information obtained form DE population and bee colony, the exploration and exploitation abilities of DEABC algorithm are balanced. The DE population uses the global best to generate offspring every generation. The bee colony acquires the best individual after few generations. The experiments are performed on six benchmark functions to compare the efficiencies of DE, ABC, PSO and DEABC. The numerical results indicate the proposed algorithm outperforms other algorithms in terms of accuracy and convergence speed.