Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Cilt: 1 Sayı: 1, 1 - 9, 30.12.2016

Öz

Kaynakça

  • [1] S. Das, S. S. Mullick, and P. N. Suganthan, "Recent advances in differential evolution - An updated survey," Swarm and Evolutionary Computation, vol. 27, pp. 1-30, 2016.
  • [2] K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution - A Practical Approach to Global Optimization: Springer, 2005.
  • [3] D. E. Goldberg, Genetic Algorithms. Reading, Massachusetts: Addison Wesley, 1989.
  • [4] C. A. C. Coello, D. A. Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multiobjective Problems. Boston: Kluwer Academic Publishers, 2003.
  • [5] K. Deb, Multiobjective Optimization using Evolutionary Algorithms: John Wiley & Sons, 2001.
  • [6] C. M. Fonseca, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary Computation, vol. 3, pp. 1- 16, 1995.
  • [7] C. A. C. Coello, "An updated survey of Ga-based multi-objective optimization techniques," ACM Computing Surveys, vol. 32, pp. 109- 143, 2000.
  • [8] K. Deb, "An efficient constraint handling method for genetic algorithms," Computer Methods in Applied Mechanics and Engineering, vol. 186, p. 28, 2000.
  • [9] A. C. A. Coello, "Use of a self adaptive penalty approach for engineering optimization problems," Computers in Industry, vol. 41, pp. 113–127, 2000.
  • [10] M. Bittermann, O. Ciftcioglu, and I. S. Sariyildiz, " Precision evolutionary optimization. Part II: Implementation and applications.," presented at the GECCO 2012, Philedelphia, 2012.
  • [11] S. Gass and T. Saaty, "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, vol. 2, p. 7, 1955.
  • [12] L. Zadeh, "Non-scalar-valued performance criteria," IEEE Trans. Automatic Control, vol. 8, p. 2, 1963.
  • [13] K. Miettinen, Nonlinear Multiobjective Optimization. Boston: Kluwer Academic, 1999.
  • [14] Y. Y. Haimes, L. S. Lasdon, and D. A. Wismer, "On a bicriterion formulation of the problems of integrated system identification and system optimization," IEEE Trans. Systems, Man, and Cybernetics, vol. 1, p. 2, 1971.
  • [15] G. Bachman and L. Narici, Functional Analysis. New York: Dover, 2000.
  • [16] J. T. Oden and L. F. Demkowicz, Applied Functional Analysis: CRC Press, 1996.
  • [17] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multi-objective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2000.
  • [18] M. S. Bittermann and O. Ciftcioglu, "Precision Evolutionary Optimization Part II: Implementation and Applications " The Journal of Cognitive Systems, vol. 1, 2016.
  • [19] S. Koziel and Z. Michalewicz, "Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization," Evolutionary Computation, vol. 7, pp. 19-44, 1999.
  • [20] J. J. Liang, T. P. Runarsson, E. Mezura-Montes, M. Clerc, P. N. Suganthan, C. A. C. Coello, and K. Deb, "Problem definitions and evaluation criteria for the CEC 2006: Special session on constrained real-parameter optimization," Journal of Applied Mechanics, vol. 41, 2006.

PRECISION EVOLUTIONARY OPTIMIZATION PART I: NONLINEAR RANKING APPROACH

Yıl 2016, Cilt: 1 Sayı: 1, 1 - 9, 30.12.2016

Öz

Theoretical foundations of a robust approach for multiobjective optimization by evolutionary algorithms are introduced. The
optimization method used is the conventional penalty function approach, which is also known as bi-objective method. The novelty of
the method stems from the dynamic variation of the commensurate penalty parameter for each objective treated as constraint. The
parameters collectively define the right slope of the tangent as to the optimal front during the search. The slope conforms to the
theoretical considerations so that the robust and fast convergence of the search is accomplished throughout the search up to micro
level in the range of 10-10 or beyond with precision as well as with accuracy thanks to a robust probabilistic distance measure
established in this work. The measure is used for nonlinear ranking among the population members of the evolutionary process, and
the method is implemented by a computer program called NS-NR developed for this research. The effectiveness of the method is
exemplified by a demonstrative computer experiment minimizing a highly non-linear, non-polynomial, non-quadratic etc. function.
The algorithm description in detail and further several applications are presented in the second part of this research. The problems
used in computer experiments are selected from the existing literature for comparison while the experiments carried out and reported
here to demonstrate the simplicity vs effectiveness of the algorithm.

Kaynakça

  • [1] S. Das, S. S. Mullick, and P. N. Suganthan, "Recent advances in differential evolution - An updated survey," Swarm and Evolutionary Computation, vol. 27, pp. 1-30, 2016.
  • [2] K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution - A Practical Approach to Global Optimization: Springer, 2005.
  • [3] D. E. Goldberg, Genetic Algorithms. Reading, Massachusetts: Addison Wesley, 1989.
  • [4] C. A. C. Coello, D. A. Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multiobjective Problems. Boston: Kluwer Academic Publishers, 2003.
  • [5] K. Deb, Multiobjective Optimization using Evolutionary Algorithms: John Wiley & Sons, 2001.
  • [6] C. M. Fonseca, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary Computation, vol. 3, pp. 1- 16, 1995.
  • [7] C. A. C. Coello, "An updated survey of Ga-based multi-objective optimization techniques," ACM Computing Surveys, vol. 32, pp. 109- 143, 2000.
  • [8] K. Deb, "An efficient constraint handling method for genetic algorithms," Computer Methods in Applied Mechanics and Engineering, vol. 186, p. 28, 2000.
  • [9] A. C. A. Coello, "Use of a self adaptive penalty approach for engineering optimization problems," Computers in Industry, vol. 41, pp. 113–127, 2000.
  • [10] M. Bittermann, O. Ciftcioglu, and I. S. Sariyildiz, " Precision evolutionary optimization. Part II: Implementation and applications.," presented at the GECCO 2012, Philedelphia, 2012.
  • [11] S. Gass and T. Saaty, "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, vol. 2, p. 7, 1955.
  • [12] L. Zadeh, "Non-scalar-valued performance criteria," IEEE Trans. Automatic Control, vol. 8, p. 2, 1963.
  • [13] K. Miettinen, Nonlinear Multiobjective Optimization. Boston: Kluwer Academic, 1999.
  • [14] Y. Y. Haimes, L. S. Lasdon, and D. A. Wismer, "On a bicriterion formulation of the problems of integrated system identification and system optimization," IEEE Trans. Systems, Man, and Cybernetics, vol. 1, p. 2, 1971.
  • [15] G. Bachman and L. Narici, Functional Analysis. New York: Dover, 2000.
  • [16] J. T. Oden and L. F. Demkowicz, Applied Functional Analysis: CRC Press, 1996.
  • [17] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multi-objective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2000.
  • [18] M. S. Bittermann and O. Ciftcioglu, "Precision Evolutionary Optimization Part II: Implementation and Applications " The Journal of Cognitive Systems, vol. 1, 2016.
  • [19] S. Koziel and Z. Michalewicz, "Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization," Evolutionary Computation, vol. 7, pp. 19-44, 1999.
  • [20] J. J. Liang, T. P. Runarsson, E. Mezura-Montes, M. Clerc, P. N. Suganthan, C. A. C. Coello, and K. Deb, "Problem definitions and evaluation criteria for the CEC 2006: Special session on constrained real-parameter optimization," Journal of Applied Mechanics, vol. 41, 2006.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Articles
Yazarlar

Özer Ciftcioglu Bu kişi benim

Şahin Serhat Şeker

Jelena Dikun Bu kişi benim

Emine Ayaz Bu kişi benim

Yayımlanma Tarihi 30 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 1 Sayı: 1

Kaynak Göster

APA Ciftcioglu, Ö., Şeker, Ş. S., Dikun, J., Ayaz, E. (2016). PRECISION EVOLUTIONARY OPTIMIZATION PART I: NONLINEAR RANKING APPROACH. The Journal of Cognitive Systems, 1(1), 1-9.