Title: An improved artificial bee colony algorithm for solving parameter identification problems

Authors: Xuemei You; Yinghong Ma; Zhiyuan Liu

Addresses: School of Management Science and Engineering, Shandong Normal University, 250014, Jinan, China; School of Engineering Management, Nanjing University, 210093, Nanjing, China ' School of Management Science and Engineering, Shandong Normal University, 250014, Jinan, China ' School of Management Science and Engineering, Shandong Normal University, 250014, Jinan, China

Abstract: Swarm intelligence algorithms (SIA) have shown excellent optimisation performance on many real world problems. Artificial bee colony (ABC) is one of the most popular SIA. However, ABC has some shortcomings. In this paper, we design an improved ABC, called IABC, which introduces a hybrid search strategy. To evaluate the optimisation ability of IABC, we run IABC on several famous benchmark functions. Experimental results show that IABC can achieve good solutions on the test functions. Finally, IABC is used to solve parameter identification problems. Two test instances are used for the simulation experiment. Results demonstrate that IABC can obtain a good matching to the target model.

Keywords: artificial bee colony; ABC; hybrid search; parameter identification; optimisation.

DOI: 10.1504/IJCSM.2017.088971

International Journal of Computing Science and Mathematics, 2017 Vol.8 No.6, pp.570 - 579

Received: 25 Apr 2017
Accepted: 01 Jun 2017
Published online: 03 Jan 2018 *

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