Markov Chain Based Artificial Bee Colony Algorithm

doi:10.13190/j.jbupt.2019-063

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (1): 54-60.doi: 10.13190/j.jbupt.2019-063

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Markov Chain Based Artificial Bee Colony Algorithm

GUO Jia^1,2, MA Chao-bin^1,2, MIAO Meng-meng², ZHANG Shao-bo²

1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
2. National Secrecy Science and Technology Evaluation Center, Beijing 100044, China

Received:2019-04-17 Online:2020-02-28 Published:2020-03-27
Supported by:

Abstract

Abstract: To overcome the shortcomings of existing local search ability and to easily obtain the local optimal solution of artificial bee colony algorithm (ABC), a new modified artificial bee colony algorithm (MABC) is proposed using the development trend of known solution space predicted by Markov Chain. The running process of the algorithm is provided through a pseudo code. The performances of the ABC and MABC are analyzed from two aspects:convergence performance and algorithm complexity. Using 10 typical functions as test cases, Experiments are carried out in four aspects:result precision, convergence speed, segmentation parameters and running time. It is shown that the MABC algorithm is superior to the ABC algorithm in terms of accuracy and convergence speed.

Key words: Markov chain, artificial bee colony algorithm, function optimization

CLC Number:

TP389

GUO Jia, MA Chao-bin, MIAO Meng-meng, ZHANG Shao-bo. Markov Chain Based Artificial Bee Colony Algorithm[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 54-60.

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Markov Chain Based Artificial Bee Colony Algorithm

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