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All issues Volume 139 (2017) MATEC Web Conf., 139 (2017) 00033 References
Open Access
Issue
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
Article Number 00033
Number of page(s) 6
DOI https://doi.org/10.1051/matecconf/201713900033
Published online 05 December 2017
  1. L. Jin, S. Li, B. Liao, and Z. Zhang, “Zeroing neural networks: A survey,” Neurocomputing, vol. 267, pp. 597–604, 2017. [CrossRef] [Google Scholar]
  2. L. Jin and Y. Zhang, “Continuous and discrete Zhang dynamics for real-time varying nonlinear optimization,” Numerical Algorithms, vol. 73, no. 1, pp. 115–140, 2016. [CrossRef] [Google Scholar]
  3. B. Liao, Y. Zhang, and L. Jin, “Taylor O(h3) discretization of ZNN models for dynamic equalityconstrained quadratic programming with application to manipulators,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 2, pp. 225–237, 2016. [CrossRef] [Google Scholar]
  4. L. Jin and Y. Zhang, “Discrete-time Zhang neural network for online time-varying nonlinear optimization with application to manipulator motion generation,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 7, pp. 1525–1531, 2016. [CrossRef] [Google Scholar]
  5. L. Jin, Y. Zhang, S. Li, and Y. Zhang, “Modified ZNN for time-varying quadratic programming with inherent tolerance to noises and its application to kinematic redundancy resolution of robot manipulators,” IEEE Trans. Ind. Electron., vol. 63, no. 11, pp. 6978–6988, 2016 [CrossRef] [Google Scholar]
  6. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Netw., 1995, 1942–1948. [CrossRef] [Google Scholar]
  7. A.H. Gandomi, X.Y. Yang, “Benchmark problems in structural optimization,” in ComputationalOptimization Methods And Algorithms, Berlin: Springer-Verlag, 2011, pp. 259–281. [CrossRef] [Google Scholar]
  8. S. He , E. Prempain, and Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems,” Eng. Opt., vol. 36, no. 5, pp. 585–605, 2004. [CrossRef] [Google Scholar]
  9. A.H. Gandomi, X.S. Yang, and A.H. Alavi, “Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems,” Eng. Comput., vol. 29, pp. 17–35, 2013. [CrossRef] [Google Scholar]
  10. O. Pauline, H.C. Sin, D.D.C.V. Sheng, S.C. Kiong, and O. K. M., “Design optimization of structural engineering problems using adaptive cuckoo search algorithm,” in Proc. 3rd Int. Conf. Control Autom. Robot., 2017, pp. 745–748. [Google Scholar]
  11. V. Garg and K. Deep, “Effectiveness of constrained laplacian biogeography based optimization for solving structural engineering design problems,” in Proc. 6th Int. Conf. Soft. Comput. Problem Solv., 2017, pp. 206–219. [CrossRef] [Google Scholar]
  12. D. Himmelblau, Applied Nonlinear Programming, New York: McGraw-Hill, 1972. [Google Scholar]
  13. Y. Zhang and S. Li, “PSA: A novel optimization algorithm based on survival rules of porcellio scaber,” Available at https://arxiv.org/abs/1709.09840. [Google Scholar]
  14. X.S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” Int. J. Bio-Inspired Comp., vol. 2, no. 2, pp. 2, pp. 78–84, 2010. [Google Scholar]
  15. K.S. Lee and Z.W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice,” Comput. MethodsAppl. Mech. Engrg., vol. 194, pp. 3902–3933, 2005. [Google Scholar]
  16. E. Sandgren, “Nonlinear integer and discrete programming in mechanical design optimization,” J.Mech. Des. ASME, vol. 112, 223–229, 1990. [CrossRef] [Google Scholar]
  17. S.J. Wu and P.T. Chow, “Genetic algorithms for nonlinearmixed discrete-integer optimization problems via meta-genetic parameter optimization,” Engrg. Optim., vol. 24, 137–159, 1995. [CrossRef] [Google Scholar]
  18. C.A.C. Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Comput. Ind., vol. 41, no. 2, pp. 113–127, 2000. [CrossRef] [Google Scholar]
  19. K. Deb, “GeneAS: a robust optimal design technique for mechanical component design”, in: D. Dasgupta, Z. Michalewicz Eds., Evolutionary Algorithms in Engineering Applications, Springer-Verlag, Berlin, 1997, pp. 497–514. [CrossRef] [Google Scholar]
  20. B.K. Kannan and S.N. Kramer, “An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design,” J. Mecha. Design, Trans. ASME, vol. 116, pp. 318–320, 1994. [Google Scholar]
  21. S. Akhtar, K. Tai, T. Ray, “A socio-behavioural simulation model for engineering design optimization,” Eng. Optmiz., vol. 34, no. 4, pp. 341–354, 2002. [CrossRef] [Google Scholar]
  22. J.F. Tsai, H.L. Li, and N.Z. Hu, “Global optimization for signomial discrete programming problems in engineering design,” Eng. Optmiz. no. 34, no. 6, pp. 613–622, 2002. [CrossRef] [Google Scholar]
  23. C.A.C. Coello and N.C. Corte´s, “Hybridizing a genetic algorithm with an artificial immune system for global optimization,” Eng. Optmiz., vol. 36, no. 5, pp. 607–634, 2004. [CrossRef] [Google Scholar]
  24. H. L. Li and C. T. Chou, “A global approach for nonlinear mixed discrete programming in design optimization,” Eng. Optmiz. vol. 22, pp. 109–122 1994. [CrossRef] [Google Scholar]
  25. A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems,” Eng. Comput. vol. 27, no. 1, pp. 155–182, 2010. [CrossRef] [Google Scholar]
  26. G. H. M. Omran and A. Salman, “Constrained optimization using CODEQ,” Chaos Soliton. Fract., vol. 42, pp. 662–668, 2009. [CrossRef] [Google Scholar]
  27. A. Homaifar, S. Lai, X. Qi, “Constrained optimization via genetic algorithms,” Simulation, vol. 62, no. 4, pp. 242–253, 1994. [CrossRef] [Google Scholar]
  28. M. Gen and R. Cheng, Genetic Algorithms and Engineering Design,Wiley, New York, 1997. [Google Scholar]
  29. G. G. Dimopoulos, “Mixed-variable engineering optimization based on evolutionary and social metaphors,” Comput. Method. Appl. Mech. Eng., vol. 196, pp. 803–817, 2007. [CrossRef] [Google Scholar]

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