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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 734193, 23 pages
Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm
Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang 44300, China
Received 19 July 2012; Revised 14 October 2012; Accepted 15 October 2012
Academic Editor: Jyh Horng Chou
Copyright © 2012 Yi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- L. Cheng and W. M. Zhang, “Optimization design of mining trucks & caliper disc brake,” Mining Research and Development, vol. 28, no. 1, pp. 41–43, 2008.
- J. Li, “Optimization design of tractor brake based on hybrid PSO,” Chinese Agricultural Mechanization, vol. 4, pp. 93–96, 2011.
- Z. H. Li, X. L. Zhang, and L. C. Guo, “Optimal design for caliper disc brake,” Machine Design and Research, vol. 25, no. 2, pp. 83–85, 2009.
- L. Chen, “Simulated annealing algorithm for function optimization and application in caliper-type brake,” Coal Mine Machinery, vol. 27, no. 10, pp. 38–41, 2006.
- Q. B. Huang, M. G. Zhou, and Y. Wang, “Structural parameters optimization design of drum brake based on genetic algorithm,” Machinery, vol. 11, pp. 24–26, 2006.
- P. Jiang and W. J. Huang, “Optimal design of disc brake based on MATLAB,” Mechanical Engineering & Automation, vol. 6, pp. 158–161, 2007.
- J. Mi and Y. L. Wu, “Multi-objective optimization design of drum-fashioned brake technology,” Machinery Design & Manufacture, vol. 1, pp. 25–26, 2007.
- J. Mi and Y. L. Wu, “The Application of multi-objective optimization method in brake design,” Journal of Mechanical Transmission, vol. 31, no. 5, pp. 71–73, 2007.
- G. L. Qin, D. M. Wang, and X. H. Chen, “Optimization design of disk brake based on an improved genetic algorithm,” Modular Machine Tool & Automatic Manufacturing Technique, vol. 7, pp. 101–103, 2011.
- H. Wang, Parametric Brake Optimization Based on Ant Colony Algorithm, Jilin University, Jilin, China, 2007.
- J. Wu and W. J. Li, “Optimal design of disk brake based on improved swarm optimization,” Machinery Design & Manufacture, vol. 4, pp. 18–20, 2007.
- R. H. Yang, “Multi-objective optimization design of auto drum-fashioned brake based on genetic algorithm of MATLAB,” Machine Tool & Hydraulics, vol. 23, pp. 91–93,97, 2011.
- M.G. Zhou, Y. Chen, J. D. Zhou, and J. Yi, “Genetic algorithm optimal design and its implementation with MATLAB for drum brake,” Journal of Hubei University of Technology, vol. 24, no. 2, pp. 78–80, 2009.
- W. Y. Wang, Automotive Design, China Machine Press, Beijing, China, 2004.
- W. X. Liu, Structural Analysis & Design of Vehicle Brake System, Tsinghua University Press, Beijing, China, 2004.
- Y. J. Shi, The Cooperative Co-Evolutionary Differential Evolution and Its Applications for Complex Layout Optimization, Dalian University of Technology, Dalian, China, 2006.
- J. Durillo, A. J. Nebro, F. Luna, and E. Alba, “Solving three-objective optimization problems using a new hybrid cellular genetic algorithm,” in Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X, pp. 661–670, 2008.
- B. Dorronsoro and E. Alba, “A simple cellular genetic algorithm for continuous optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC'06), pp. 2838–2844, IEEE Computer Society, Vancouver, Canada, July 2006.
- Deb, K. L. Thiele, M. Laumanns, and E. Zitzler, “Scalable test problems for evolutionary multi-objective optimization,” Tech. Rep. 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 2001.
- A. Vicini and D. Quagliarella, “Multipoint transonic airfoil design by means of a multiobjective genetic algorithm,” Tech. Rep. AIAA-97-0082, AIAA, Washington, DC, USA, 1997.
- H. Tamaki, M. Mori, M. Araki, Y. Mishima, and H. Ogai, “Multi-criteria optimization by genetic algorithms: a case of scheduling in hot rollingprocess,” in Proceedings of the 3rd Conference of the Association of Asian-Pacific Operational Research Societies with IFORS (APORS'94), pp. 374–381, World Scientifc, Fukuoka, Japan, July 1994.
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
- A. J. Nebro, J. J. Durillo, F. Luna, B. Dorronsoro, and E. Alba, “MOCell: a cellular genetic algorithm for multiobjective optimization,” International Journal of Intelligent Systems, vol. 24, no. 7, pp. 726–746, 2009.
- Corne, D. W. N. R. Jerram, J. D. Knowles, M. J. Oates, and J. Martin, “PESA-II: Region-based selection in evolutionary multi-objective optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'01), pp. 283–290, Morgan Kaufmann, San Francisco, Calif, USA, July 2001.
- J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the Pareto archived evolution strategy,” Evolutionary Computation, vol. 8, no. 2, pp. 149–172, 2000.
- D. A. van Veldhuizen and G. B. Lamomt, “On measuring multiobjective evolutionary algorithm performance,” in Proceedings of the Congress on Evolutionary Computation (GECCO'00), pp. 204–211, Morgan Kaufmann, Las Vegas, Nevada, USA, 2000.
- E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999.
- K. Deb and R. B. Agrawal, “Simulated binary crossover for continuous search space,” Complex Systems, vol. 2, pp. 115–148, 1995.
- K. Zielinski, P. Weitkemper, R. Laur, and K. D. Kammeyer, “Parameter study for differential evolution using a power allocation problem including interference cancellation,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC'06), pp. 1857–1864, IEEE Computer Society, Vancouver, Canada, July 2006.