82
Views
2
CrossRef citations to date
0
Altmetric
Original Paper

Differential evolutionary algorithm in the construction process of optimal experimental designs

&
Pages 7733-7743 | Received 25 Feb 2016, Accepted 11 Oct 2016, Published online: 22 May 2017

References

  • Atkinson, A. C. ( 2007). Optimum Experimental Designs, with SAS (Oxford Statistical Science Series). Vol. 34. Oxford: Oxford University Press.
  • Boussaid, I., Lepagnot, J., Siarry, P. ( 2013). A survey on optimization metaheuristics. Information Sciences 237:82–117.
  • Dong, C.-R., Ng, W. W., Wang, X.-Z., Chan, P. P., Yeung, D. S. ( 2014). An improved differential evolution and its application to determining feature weights in similarity-based clustering. Neurocomputing 146:95–103.
  • Fedorov, V. ( 1972). Theory Of Optimal Experiments (Probability and Mathematical Statistics). Burlington: Elsevier Science. Available at: https://www.elsevier.com/books/theory-of-optimal-experiments/fedorov/978-0-12-250750-2.
  • Hamada, M., Martz, H. F., Reese, C. S., Wilson, A. G. ( 2001). Finding near-optimal Bayesian experimental designs via genetic algorithms. The American Statistician 55(3):175–181.
  • Hedayat, A., Zhou, Y., Yang, M. ( 2014). Optimal designs for some selected nonlinear models. Journal of Statistical Planning and Inference 154:102–115.
  • Hegerty, B., Hung, C.-C., and Kasprak, K. ( 2009). A comparative study on differential evolution and genetic algorithms for some combinatorial problems. In: Proceedings of 8th Mexican International Conference on Artificial Intelligence. MICAI, pp. 13. Available at http://www.micai.org/2009/proceedings/.../cd/ws.../paper88.micai09.pdf.
  • Jin, R., Chen, W., Sudjianto, A. ( 2005). An efficient algorithm for constructing optimal design of computer experiments. Journal of Statistical Planning and Inference 134(1):268–287.
  • Li, H., Zhang, L. ( 2014). A discrete hybrid differential evolution algorithm for solving integer programming problems. Engineering Optimization 46(9):1238–1268.
  • Lin, C. D., Anderson-Cook, C. M., Hamada, M. S., Moore, L. M., Sitter, R. R. ( 2014). Using genetic algorithms to design experiments: A review. Quality and Reliability Engineering International 31(2):155–167.
  • Mohanty, B., Panda, S., Hota, P. ( 2014). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. International Journal of Electrical Power & Energy Systems 54:77–85.
  • Montgomery, J., Chen, S. ( 2010). An analysis of the operation of differential evolution at high and low crossover rates. In: 2010 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1–8. Available at: http://ieeexplore.ieee.org/document/5586128/.
  • Schneider, E. R., Krohling, R. A. ( 2014). A hybrid approach using TOPSIS, differential evolution, and tabu search to find multiple solutions of constrained non-linear integer optimization problems. Knowledge-Based Systems 62:47–56.
  • Silvey, S. ( 2013). Optimal Design: An Introduction to the Theory for Parameter Estimation (Ettore Majorana International Science Series). Netherlands: Springer.
  • Storn, R., Price, K. ( 1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4):341–359.
  • Weber, M., Neri, F., Tirronen, V. ( 2013). A study on scale factor/crossover interaction in distributed differential evolution. Artificial Intelligence Review 39(3):195–224.
  • Wynn, H. P. ( 1970). The sequential generation of d-optimum experimental designs. Annals of Mathematical Statistics 41(5):1655–1664.
  • Yildiz, A., Öztürk, N., Kaya, N., Öztürk, F. ( 2003). Integrated optimal topology design and shape optimization using neural networks. Structural and Multidisciplinary Optimization 25(4):251–260.
  • Yildiz, A. R. ( 2008). Optimal structural design of vehicle components using topology design and optimization. Materials Testing 50(4):224–228.
  • Yildiz, A. R. ( 2013a). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence 26(1):327–333.
  • Yildiz, A. R. ( 2013b). Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Applied Soft Computing 13(3):1433–1439. (Hybrid evolutionary systems for manufacturing processes).
  • Yildiz, A. R. ( 2013c). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing 13(3):1561–1566. (Hybrid evolutionary systems for manufacturing processes).
  • Yildiz, A. R., Kaya, N., Ozturk, F., Alankus, O. ( 2004). Optimal design of vehicle components using topology design and optimisation. International Journal of Vehicle Design 34(4):387–398.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.