Abstract
Aiming at the problem that the traditional multi-objective optimization method is sensitive to the shape of the Pareto frontier and defect that can not guarantee to obtain Pareto decision solution, bases on the idea of system decision, combines the grey system theory and fuzzy comprehensive evaluation, establish grey fuzzy optimal model of Pareto solution and make a math demonstration about its correctness, while it gives the corresponding genetic algorithm. Project examples show that the model is practical and effective.
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
Cours, P.V.: D’ Economic Politique, vol. I and II. F. Rouge, Lausanne (1896)
Ritzel, B.J., Wayland Eheart, J., Ranjithan, S.: Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resources Research 30(5), 1589–1603 (1994)
Fujita, K., Hirokawa, N., Akagi, S., et al.: Multi-objective Optimal Design of Automotive Engine Using Genetic Algorithm
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Swiss Federal Institute of Technology, Switzerland (1999)
Deb, K., Pratap, A., Agarwal, S., Meyarivn, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Collo, C.A.C.: Handling Preferences in Evolutionary Multiobjective Optimization: A survey. In: 2000 Congress on Evolutionary Computation, vol. 1, pp. 30–37. IEEE Service Center, Piscataway (2000)
Sifeng, Guo, T., Dang, Y.: Grey system theory and application. Science, Beijing (1999)
Chen, S.: Project hydrology water resources system fuzzy set analysis theory and practice. Dalian University of Technology, Dalian (1998)
Sun, G.: Optimal design and application. China Communications, Beijing (2000)
Fei, Y., Li, N., Zheng, X.: Improved genetic algorithm based on structure and adaptive parameters. Shenyang University of Architecture 22(2), 338–340 (2006)
Xie, N., Fang, H., Bao, J.: The application of Game decision analysis in multi-objective design of compensative sheave block luff mechanism. Mechanical Strength 27(2), 202–206 (2005)
Xie, N., Wang, Q.: The Multi-objective fuzzy Optimization of pulley block compensation luffing mechanism based on genetic algorithm. Mechanical Transmission 27(1), 30–32 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ye, F., Tianen, Z., Haiyang, Y. (2011). Multi-objective Optimization of Crane Luffing Mechanism Based on Gray Fuzzy Optimal Model. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23220-6_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-23220-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23219-0
Online ISBN: 978-3-642-23220-6
eBook Packages: Computer ScienceComputer Science (R0)