¹School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102600,China.
²Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550025, PR China.

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Abstract—Artificial bee colony (ABC) is one of very effective intelligence optimization algorithms, which has good performance in solving global optimization problems(GOPs). However, the ABC algorithm performs relatively poorly in complex GOPs for some weaknesses, such as slow convergence and poor exploitation. An improved artificial bee colony algorithm named SABC algorithm is proposed, which combines the ABC algorithm and the simplex method for GOPs. In SABC algorithm, the simplex method is employed to update the food sources which cannot be improved after limit updates in the ABC algorithm. The proposed SABC algorithm takes not only the advantages of directness, simpleness and small calculation amount of simplex method, but also the strong global search ability of ABC algorithm. The accurate and rapid local research ability of the simplex method can guarantee to avoid some blind meaningless iteration in the ABC algorithm. The validity has been verified by the numerical experiments on 9 benchmark GOPs. The numerical result show that the proposed SABC algorithm has faster convergence speed and better convergence accuracy compared to the basic ABC algorithm.

Index Terms—Artificial bee colony algorithm, simplex method, numerical experiments.

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Cite: Xufang Zhao, Ximing Liang, Long Wen, "The Artificial Bee Colony Algorithm Improved with Simplex Method," Journal of Software vol. 13, no. 6, pp. 350-359, 2018.