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Journal of Applied Mathematics
Volume 2013, Article ID 912056, 9 pages
Research Article

A Cooperative Coevolutionary Cuckoo Search Algorithm for Optimization Problem

1Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning, Guangxi 530006, China
2College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, Guangxi 530006, China

Received 24 May 2013; Accepted 8 July 2013

Academic Editor: Xin-She Yang

Copyright © 2013 Hongqing Zheng and Yongquan Zhou. 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.


Taking inspiration from an organizational evolutionary algorithm for numerical optimization, this paper designs a kind of dynamic population and combining evolutionary operators to form a novel algorithm, a cooperative coevolutionary cuckoo search algorithm (CCCS), for solving both unconstrained, constrained optimization and engineering problems. A population of this algorithm consists of organizations, and an organization consists of dynamic individuals. In experiments, fifteen unconstrained functions, eleven constrained functions, and two engineering design problems are used to validate the performance of CCCS, and thorough comparisons are made between the CCCS and the existing approaches. The results show that the CCCS obtains good performance in the solution quality. Moreover, for the constrained problems, the good performance is obtained by only incorporating a simple constraint handling technique into the CCCS. The results show that the CCCS is quite robust and easy to use.