Abstract
The new concept of sky luminance distributions which is modeling skies under a wide range of occurrences from the overcast sky to cloudless situations without or with sunlight respectively was proposed by CIE. The numerical expressions of this concept contain five adjustable parameters (a, b, c, d, e). Each type of sky proposed by CIE represents one combination of the parameters. In this paper, according to the research on the characteristics of the numerical expressions, for a measured sky type, a heuristic algorithm for solving complex optimization problems —ant colony optimization will be used to analyze and optimize the influencing factors of the sky luminance and finally get its parameters value, the experiment results show that it has high accuracy and good effect.
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.
Similar content being viewed by others
References
Moon, P., Spencer, D.E.: Illumination from a non-uniform sky. Illum. Eng. 37, 707–726 (1942)
Darula, S., Kittler, R.: A catalogue of fifteen sky luminance patterns between the CIE standard skies. In: Proc. 24th of the CIE Session, vol. 1, part 2, pp. 7–9. CIE Publ. 133, Warsaw (1999)
Dorigo, M., Gambardella, L.M.: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Trans. on Evolutionary Computation 1(1), 53–66 (1997)
Xing, J.Q., Zhu, Q.S., Guo, P.: Research on ant colony clustering combination method. Computer Engineering and Application 45, 146–148 (2009)
Gambardella, L.M., Taillard, E.D., Dorigo, M.: Ant Colonies for the Quadratic Assignment Problem. J. Oper. Res. Soci. 50, 167–176 (1999)
Zhou, J.X., Yang, W.D., Li, Q.: Improved Ant Colony Algorithm and Simulation for Continuous Function Optimization. Journal of System Simulation 21, 1685–1688 (2009)
Wittkopf, S.K.: Analysing sky luminance scans and predicting frequent sky patterns in Singapore. Lighting Res. Technol. 39, 31–51 (2007)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5, 137–172 (1999)
Xiao, J., Li, L.P.: A hybrid ant colony optimization for continuous domains. Expert Systems with Applications 38, 11072–11077 (2011)
Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155–1173 (2008)
Yang, L., Fu, Z.Q., Wang, D., Li, H.L., Xia, J.B.: An improved ant colony algorithm for continuous space optimization. In: Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, pp. 1829–1843. IEEE Press, Qingdao (2010)
Wang, J., Xiao, Q., Zhang, J.: Improved ant colony optimization for solving constrained continuous function optimization problems. J. Computer Engineering and Design 31, 1027–1031 (2010)
Zhao, H.Y., Li, G.C., Cui, J.: Continuous and colony optimization based on normal distribution model of pheromone. Computer Engineering and Applications 46, 54–56 (2010)
Sun, H.Y., Chen, L.: A new approach for solving continuous optimization using ant colony optimization. Journal of ShanDong University 39, 24–30 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, P., Zhu, L., Liu, Z., He, Y. (2012). An Ant Colony Algorithm for Solving the Sky Luminance Model Parameters. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-34062-8_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34061-1
Online ISBN: 978-3-642-34062-8
eBook Packages: Computer ScienceComputer Science (R0)