Accelerating artificial bee colony algorithm using elite information

被引：0

作者：

Zhou X. ^{[1
]}

Wu Y. ^{[1
]}

Wu S. ^{[1
]}

Zhong M. ^{[1
]}

Wang M. ^{[1
]}

机构：

[1] School of Computer and Information Engineering, Jiangxi Normal University, Nanchang

来源：

International Journal of Innovative Computing and Applications | 2022年 / 13卷 / 5-6期

基金：

中国国家自然科学基金;

关键词：

ABC; artificial bee colony; elite information; exploitation; exploration; solution search equation;

D O I：

10.1504/ijica.2022.128440

中图分类号：

学科分类号：

摘要：

In nature, the foraging behaviour of bee colony is always guided by some elite honeybees with the aim of maximising the overall nectar amount. Being inspired by this phenomenon, we propose an improved artificial bee colony (ABC) variant by using elite information. In our approach, as the main way of generating new offspring, two novel solution search equations are developed based on utilising elite information, which has the advantages of accelerating convergence rate. Moreover, to preserve the search experience of the scout bee phase, a new reinitialisation method is proposed based on using elite information. Extensive experiments are conducted on the CEC 2013 and CEC 2015 test suites, and other four relevant ABC variants are included in the comparison. The results show that our approach has better performance in terms of convergence speed and result accuracy. Copyright © 2022 Inderscience Enterprises Ltd.

引用

页码：325 / 335

页数：10

共 35 条

[1]

Aslan S., Badem H., Karaboga D., Improved quick artificial bee colony (iqABC) algorithm for global optimization, Soft Computing, 23, 24, pp. 13161-13182, (2019)

[2]

Back T., Fogel D.B., Michalewicz Z., Handbook of evolutionary computation, Release, 97, 1, (1997)

[3]

Cui L., Li G., Lin Q., Chen J., Lu N., Zhang G., Artificial bee colony algorithm based on neighboring information learning, International Conference on Neural Information Processing, pp. 279-289, (2016)

[4]

De Jong K., Evolutionary computation: a unified approach, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 327-342, (2020)

[5]

Dorigo M., Birattari M., Stutzle T., Ant colony optimization, IEEE Computational Intelligence Magazine, 1, 4, pp. 28-39, (2006)

[6]

Fogel D.B., Evolutionary Computation: Toward A New Philosophy of Machine Intelligence, (2006)

[7]

Friedman A., Free boundary problems in science and technology, Notices of the AMS, 47, 8, pp. 854-861, (2000)

[8]

Gao W., Liu S., A modified artificial bee colony algorithm, Computers & Operations Research, 39, 3, pp. 687-697, (2012)

[9]

Gao W., Sheng H., Wang J., Wang S., Artificial bee colony algorithm based on novel mechanism for fuzzy portfolio selection, IEEE Transactions on Fuzzy Systems, 27, 5, pp. 966-978, (2018)

[10]

Gorkemli B., Karaboga D., A quick semantic artificial bee colony programming (QSABCP) for symbolic regression, Information Sciences, 502, pp. 346-362, (2019)

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