Applications of Intelligent Evolutionary Algorithms in Optimal Automation System Design

Tung-Kuan Liu, Jyh-Horng Chou

Abstract


This paper proposes an intelligent evolutionary algorithm that can be applied in the design of optimal automation systems, and employs a multimodal six-bar mechanism optimization design, job shop production scheduling for the fishing equipment industry, and dynamic real-time production scheduling system design cases to show how the technique developed in this paper is highly effective at resolving optimal automation system design problems. Major breakthroughs in artificial intelligence continue to be made in the wake of advanced information technology developments, and the field of intelligent evolutionary algorithms has attracted a particularly large amount of attention from researchers and users in the artificial intelligence community. The successful optimization of automation system design requires interdisciplinary integration, and further requires the use of actual cases, verification, and improvement to ensure implementation in real-world applications.

Keywords


Intelligent Evolutionary Algorithms; Optimized Automation System Design; multimodal six-bar mechanism; artificial intelligence

References


  1. Y. H. Juan, "Application of simulation and genetic algorithm to the identical machine scheduling problem," M.S. thesis, Department of Industrial Engineering and Management, Yuan Ze University, Taiwan, 2002.
  2. T. K. Liu, Production control information system course lecture notes. Taiwan: Department of Mechanical Engineering, National Kaohsiung First University of Science and Technology, 2007.
  3. T. Genʼichi, C. Subir, and T. Shin, Robust engineering. New York: McGraw-Hill, 2000.
  4. T. K. Liu, J. T. Tsai, and J. H. Chou, "Improved genetic algorithm for the job-shop scheduling problem," The International Journal of Advanced Manufacturing Technology, vol. 27, no. 9-10, pp. 1021-1029, 2005.
    doi: 10.1007/s00170-004-2283-4
  5. J. T. Tsai, T. K. Liu, and J. H. Chou, "Hybrid taguchi-genetic algorithm for global numerical optimization," IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 365-377, 2004.
    doi: 10.1109/TEVC.2004.826895
  6. J. H. Chou, W. H. Liao, and J. J. Li, "Application of Taguchi-genetic method to design optimal grey-fuzzy controller of a constant turning force system," in 15th Annual Conference of the Chinese Society of Mechanical Engineers, Taiwan, 1998, pp. 31-38.
  7. Y. Tsujimura, Y. Mafune, and M. Gen, "Effects of symbiotic evolution in genetic algorithms for job-shop scheduling," in, 34th Annual Hawaii International Conference on System Sciences, Los Alamitos, CA, USA, 2001, pp. 3026-3026.
    doi: 10.1109/HICSS.2001.926320
  8. M. Gen and R. Cheng, Genetic algorithms and engineering design, 1st ed. New York: Wiley-Interscience, 1997.
  9. Y. M. Kuo, "Applying niche genetic algorithms to optimizing motion control of multi-DOF robots," M.S. thesis, Graduate Institute of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 2008.
  10. G. Shl, "A genetic algorithm applied to a classic job-shop scheduling problem," International Journal of Systems Science, vol. 28, no. 1, pp. 25-32, 1997.
    doi: 10.1080/00207729708929359
  11. J. H. Chou, Experimental design and quality engineering lecture notes. Taiwan: Graduate Institute of Systems and Control, National Kaohsiung First University of Science and Technology, 2005.
  12. K. J. Waldron and G. L. Kinzel, Kinematics, dynamics, and design of machinery, 2nd ed. New York: Wiley, 2004.
  13. R. Norton, Fundamentals of machine design. New York: McGraw-Hill, 2004.
  14. M. A. Laribi, A. Mlika, L. Romdhane, and S. Zeghloul, "A combined genetic algorithm-fuzzy logic method (GA-FL) in mechanisms synthesis," Mechanism and Machine Theory, vol. 39, no. 7, pp. 717-735, 2004.
    doi: 10.1016/j.mechmachtheory.2004.02.004
  15. I. M. Huang, "Intelligent design of six-bar linkage mechanisms using multi-objective genetic algorithms," M.S. thesis, Graduate Institute of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 2009.
  16. H. Zhou, "Dimensional synthesis of adjustable path generation linkages using the optimal slider adjustment," Mechanism and Machine Theory, vol. 44, no. 10, pp. 1866-1876, 2009.
    doi: 10.1016/j.mechmachtheory.2009.03.010
  17. D. Y. Dai, "Optimal design of multi-modal six-bar linkage mechanism using niche genetic algorithms," M.S. thesis, Graduate Institute of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 2009.
  18. X. Yu and B. Meng, "Research on dynamics in group decision support systems based on multi-objective genetic algorithms," in International Conference on Service Systems and Service Management, Troyes, France, 2006, pp. 895-900.
    doi: 10.1109/ICSSSM.2006.320750
  19. X. Hu and C. Xie, "Niche genetic algorithm for robot path planning," in Third International Conference on Natural Computation (ICNC), Haikou, China, 2007, pp. 774-778.
    doi: 10.1109/ICNC.2007.502
  20. Y. L. Zheng and D. M. Lei, "Hybrid niche genetic algorithm for set covering problem," in, International Conference on Machine Learning and Cybernetics, Hong Kong, 2007, pp. 1009-1013.
    doi: 10.1109/ICMLC.2007.4370290
  21. B. Huang, Z. Wang, and Y. Xu, "Multi-objective genetic algorithm for hybrid electric vehicle parameter optimization,"in International Conference on Intelligent Robots and Systems, Beijing, China, 2006, pp. 5177-5182.
    doi: 10.1109/IROS.2006.281654
  22. Y. Tsujimura, M. Gen, and R. Cheng, "Improved genetic algorithms for solving job-shop scheduling problem," Engineering Design and Automation, vol. 3, no. pp. 133-144, 1997.
  23. Y. C. Chen, "Applications of multi-objective genetic algorithms for real-world optimization in production scheduling," M.S. thesis, Graduate Institute of Mechanical and Automation Engineering, National Kaohsiung First University of Science and Technology, 2008.
  24. T. K. Liu, J. T. Tsai, J. H. Chou, and C. H. Lai, "Job-shop scheduling problems by using an improved genetic algorithm," in Annual Conference of the Society of Instrument and Control Engineers (SCIE), Okayama, Japan, 2005, pp. 944-949.


Full Text: PDF HTML

Refbacks

  • There are currently no refbacks.


Copyright © 2011-2017  AUSMT   ISSN: 2223-9766