ICICA 2012: Information Computing and Applications pp 315-322 | Cite as
Optimization of Lifting Points of Large-Span Steel Structure Based on Evolutionary Programming
Abstract
To design the lifting points of large-span steel structure when the various compatibility equations are undefined in the lifting process, the programs based on improved evolutionary programming are developed by MATLAB. Lifting points design is to determine the comprehensive optimal strategy on number and distribution of lifting points, among which the minimum strain energy theory is mentioned and the secondary development technology of ANSYS-APDL is used. The performance and efficiency of the algorithms in different optimization ideas (hiberarchy optimization and synchronic optimization) and methods (the particle swarm optimization and evolutionary programming) are compared, the results indicate that the improved evolutionary programming method based on synchronic optimization idea is satisfactory and provides a new but more effective method.
Keywords
Large-span steel structure Lifting points design Synchronic optimization Evolutionary programming Single-point mutationPreview
Unable to display preview. Download preview PDF.
References
- 1.Rui, Y.: Research on the Integral Hoisting Construction Method and the Controls Parameters. Ph.D. Dissertation of Tongji University, China (2008)Google Scholar
- 2.Rajasekaran, S., Annet, D., Sang Choo, Y.: Optimal Locations for Heavy Lifts for Offshore Platforms. Asian Journal of Civil Engineering (Building and Housing) 9(6), 605–627 (2008)Google Scholar
- 3.Bing, Z.: Research on Layout of Hoisting and Temporary Support in the Construction Process of Long-span Space Structures. Master Dissertation of Zhejiang University, China (2006)Google Scholar
- 4.Guo, Y.-L., Cui, X.-Q.: Key Technical Problems and Discussion in Construction Process of Large Span Steel Structures. Industrial Construction 34(12), 1–5 (2004)MathSciNetGoogle Scholar
- 5.Chen, B.-W.: The optimal research for the lift points of large span steel structure. Master Thesis, Dalian University of Technology (2011)Google Scholar
- 6.Fogel, D.B.: An Introduction to Simulated Evolutionary Optimization. IEEE Trans. on Neural Networks 5(1), 3–14 (1994)CrossRefGoogle Scholar
- 7.Yun, Q.-X.: Evolutionary Algorithm. Metallurgical Industry Press, Beijing (2000)Google Scholar
- 8.Yan, X.: Actuality and Developmental Trend of the Evolutionary Programming. Journal of Heze Teachers College 25(4), 23–26 (2003)Google Scholar
- 9.Lin, D., Li, M.-Q., Kou, J.-S.: Two Methods to Prevent and Overcome Premature Convergence in Evolutionary Programming. Journal of Systems Engineering 16(3), 211–216 (2001)Google Scholar
- 10.Wang, X.-J., Xiang, D., Jiang, T., Lin, C.-S., Gong, S.-G., Fang, X.: A Novel Bi-Group Evolutionary Programming. Chinese Journal of Computers 29(5), 835–840 (2006)Google Scholar
- 11.Chellapilla, K.: Combining Mutation Operators in Evolutionary Programming. IEEE Transactions on Evolutionary Computation 2(3), 91–96 (1998)CrossRefGoogle Scholar
- 12.Ji, M., Tang, H., Guo, J.: A Single-point Mutation Evolutionary Programming. Information Processing Letters (90), 293–299 (2004)MathSciNetMATHCrossRefGoogle Scholar
- 13.Jiang, S.X., et al.: Advanced ANSYS Finite Element Analysis Method and Application Examples. China Water Power Press, Beijing (2006)Google Scholar
- 14.Boyi Team: APDL Parametric Finite Element Analysis Technology and Application Examples. China Water Power Press, Beijing (2004)Google Scholar
- 15.Bo, L.: Particle Swarm Optimization Algorithm and its Engineering Application. Publishing House of Electronics Industry, Beijing (2010)Google Scholar
- 16.Poli, R., Kennedy, J., Blackwell, T.: Particle Swarm Optimization-An Overview. Swarm Intell. 1, 33–57 (2007)CrossRefGoogle Scholar
- 17.Fang, G.: Research on Intelligent Particle Swarm Optimization Algorithm. Ph.D. Dissertation of Harbin Institute of Technology (2008)Google Scholar