An Improved Artificial Bee Colony Algorithm with Memory

doi:10.13190/j.jbupt.2017-037

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2017, Vol. 40 ›› Issue (5): 61-66.doi: 10.13190/j.jbupt.2017-037

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An Improved Artificial Bee Colony Algorithm with Memory

DU Zhen-xin^1,2, LIU Guang-zhong², HAN De-zhi²

1. School of Computer Information Engineering, Hanshan Normal University, Guangdong Chaozhou 521041, China;
2. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Received:2017-03-27 Online:2017-10-28 Published:2017-11-21

Abstract

Abstract: Artificial bee colony algorithm with memory (ABCM) memorizes successful coefficients and neighbors to guide the further foraging of the artiflcial bees. ABCM consumes many function evaluations to converge to the attractors and use the same rejection coefficients as last time, which easily results in slow convergence, low population diversity and falling into the local minima. In the improved ABCM (IABCM), the candidates converge to the attractors consuming only one function evaluation, and the candidate will replace the current solution if the former is better than the latter. Otherwise, the memory will be deleted directly. By doing so, IABCM can get the most profit at a minimum cost. When the rejection coefficients are used, the numeric parts will be regenerated randomly to enhance the diversity and randomness, which is beneficial to help the algorithm to escape the local minima. Experiments on 22 functions with different characteristics demonstrate that the IABCM is significantly better than ABC and ABCM in terms of solution quality and convergence speed.

Key words: artiflcial bee colony, memory, convergence speed, function optimization

CLC Number:

TP18

DU Zhen-xin, LIU Guang-zhong, HAN De-zhi. An Improved Artificial Bee Colony Algorithm with Memory[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(5): 61-66.

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An Improved Artificial Bee Colony Algorithm with Memory

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