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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 546181, 12 pages -

Research ArticleSchool of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, China

Received 13 July 2015; Revised 1 November 2015; Accepted 8 November 2015

Academic Editor: Miguel A. Salido

Copyright © 2015 Dan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Group search optimizer GSO which is an effective evolutionary algorithm has been successfully applied in many applications. However, the purely stochastic resampling or selection mechanism in GSO leads to long computing time and premature convergence. In this paper, we propose a diversity-guided group search optimizer DGSO with opposition-based learning OBL to overcome these limitations. Opposition-based learning is utilized to accelerate the convergence rate of GSO, while the diversity guidance DG is used to increase the diversity of population. When compared with the standard GSO, a novel operator using OBL and DG is developed for the population initialization as well as the generation jumping. A comprehensive set of 19 complex benchmark functions is used for experiment verification and is compared to the original GSO algorithm. Numerical experiments show that the proposed DGSO leads to better performance in comparison with the standard GSO in the convergence rate and the solution accuracy.

Autor: Dan Wang, Congcong Xiong, and Xiankun Zhang



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