A new hybrid artificial bee colony algorithm for global optimization
To further improve the performance of artificial bee colony algorithm (ABC), a new hybrid ABC (HABC) for global optimization is proposed via exploring six initialization methods. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is also developed. The algorithms are applied to 27 benchmark functions with various dimensions to verify its performance. Numerical results demonstrate that the proposed algorithms outperforms the ABC in global optimization problems, especially the HABC algorithm with random initialization and HABCO algorithm with orthogonal initialization.
Keywords: Artificial bee colony algorithm, Initialization methods, Search mechanism, Differential evolution.
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ABOUT THE AUTHORS
Xiangyu Kong
Department of Applied Mathematics, Xidian University
Sanyang Liu
Department of Applied Mathematics, Xidian University
Zhen Wang
Institute of Information and System Computation Science, Beifang University of Nationalities
Xiangyu Kong
Department of Applied Mathematics, Xidian University
Sanyang Liu
Department of Applied Mathematics, Xidian University
Zhen Wang
Institute of Information and System Computation Science, Beifang University of Nationalities
