Election Campaign Optimization Algorithm for Design of Pressure Vessel

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Abstract:

Election Campaign Optimization algorithm is a new heuristic algorithm, it works by simulating the behavior that the election candidates pursue the highest support in election campaign. In this paper, the problem of optimal designing a pressure vessel is selected to verify the validity of Election Campaign Optimization algorithm. The computing result shows that Election Campaign Optimization algorithm can find the global optimal solutions for design problem.

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Periodical:

Advanced Materials Research (Volumes 308-310)

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15-20

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August 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] D. H. Wolpert, W. G. Macready, "No free lunch theorems for optimization". IEEE Transactions on Evolutionary Computation, vol. 1, p.67–82, 1997.

DOI: 10.1109/4235.585893

Google Scholar

[2] J. Li, W.G. Lv, M. H. Hou, "Path planning for mobile robot based on Election Algorithm," Machine Tool & Hydraulics, vol. 37, p.30–31,68, 2009.

Google Scholar

[3] W.G. Lv, J.H. Du, J. Li, etc., "Optimization of double universal coupling using competitive algorithms," Journal of Gongdong Non-Ferrous Metals, vol. 1, p.221–223, 2007.

Google Scholar

[4] L.L. Zheng, W.G. Lv, "The optimization design of machine-tools spindle structure based on competitive algorithm," Machinery Design & Manufacture, vol. 8, p.35–37, 2006.

Google Scholar

[5] W.G. Lv, Q.H. Xie, P. Tang, etc., "An experimental study of benchmarking functions for Election Campaign Algorithm," 2010 international Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010). Changsha, 2010.

DOI: 10.1109/icmtma.2010.585

Google Scholar

[6] W.G. Lv, Q.H. Xie, Z.Y. Liu, etc., "Election Campaign Algorithm," 2nd international Asia Conference on Information in Control, Automation and Robotics(CAR 2010). Wuhaun, 2010.

DOI: 10.1109/car.2010.5456623

Google Scholar

[7] C.A. Coello, "Use of a self-adaptive penalty approach for engineering optimization problems", Computers in Industry, vol.41, No.2, pp.113-127, 2000.

DOI: 10.1016/s0166-3615(99)00046-9

Google Scholar

[8] A.A. Homaifar, S.H.Y. Lai, X. Qi, "Constrained optimization via genetic algorithm," Simulation, vol.62, no.4, pp.242-254, 1994.

DOI: 10.1177/003754979406200405

Google Scholar

[9] K. Deb, "GeneASS: a robust optimal design technique for mechanical component design," in D. Dasgupta and Z. Michalewicz (eds.) Evolutionary Algorithms in Engineering Applications, Berlin: Springer-Verlag, pp.497-514, 1997.

DOI: 10.1007/978-3-662-03423-1_27

Google Scholar

[10] K. Deb, "Optimal design of a welded beam via genetic algorithms," AIAA journal, vol.29, no.11, pp.2013-2015, 1991.

DOI: 10.2514/3.10834

Google Scholar

[11] J.S. Arora, "Introduction to optimum design", New York: McGrow-Hill, 1

Google Scholar