Abstract
This paper designs a new kind of structured population and evolutionary operators to form a novel algorithm, Organizational Evolutionary Algorithm (OEA), for solving constrained optimization problems. A simple and non problem-dependent technique is incorporated into OEA to handle the constraints. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate the interaction among organizations. In experiments, 4 well-studied engineering design problems are used to test the performance of OEA. The results show that OEA obtains good results both in the solution quality and the computational cost.
This work was supported by the National Natural Science Foundation of China under Grant 60502043.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Coase, R.H.: The Firm, the Market, and the Law. University of Chicago Press, Chicago (1988)
Wilcox, J.R.: Organizational Learning within a Learning Classifier System, Master thesis, University of Illinois, Also Tech. Report No. 95003 IlliGAL (1995)
Jiao, L., Jiu, J., Zhong, W.: An organizational coevolutionary algorithm for classification. IEEE Trans. Evol. Comput. 10(1), 67–80 (2006)
Montes, E.M., Coello, C.A.C.: A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans. Evol. Comput. 9(1), 1–17 (2005)
Ray, T., Liew, K.M.: Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Trans. Evol. Comput. 7(4), 386–396 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Zhong, W. (2006). Constrained Optimization Using Organizational Evolutionary Algorithm. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_39
Download citation
DOI: https://doi.org/10.1007/11903697_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)