Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2016, Cilt: 1 Sayı: 1, 19 - 27, 30.12.2016

Öz

Kaynakça

  • [1] C. M. Fonseca, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary Computation, vol. 3, pp. 1- 16, 1995.
  • [2] C. A. C. Coello, "An updated survey of Ga-based multi-objective optimization techniques," ACM Computing Surveys, vol. 32, pp. 109- 143, 2000.
  • [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] K. Deb and R. Datta, "A bi-objective constrained optimization algorithm using a hybrid evolutionary and penalty function approach," Engineering Optimization, vol. 45, pp. 503-527, 2013.
  • [7] 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.
  • [8] C. A. C. Coello, "Use of a self-adaptive penalty approach for engineering optimization problems," Ciomputers in Industry, vol. 41, pp. 113-127, 2000.
  • [9] S. Gass and T. Saaty, "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, vol. 2, p. 7, 1955. [10] L. Zadeh, "Non-scalar-valued performance criteria," IEEE Trans. Automatic Control, vol. 8, p. 2, 1963. [11] K. Miettinen, Nonlinear Multiobjective Optimization. Boston: Kluwer Academic, 1999.
  • [12] 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.
  • [13] P. L. Meyer, Introductory Probability and Statistical Applications: Addison-Wesley, 1970.
  • [14] G. Bachman and L. Narici, Functional Analysis. New York: Dover, 2000.
  • [15] J. T. Oden and L. F. Demkowicz, Applied Functional Analysis: CRC Press, 1996.
  • [16] O. Ciftcioglu, M. S. Bittermann, and I. S. Sariyildiz, "Precision Evolutionary Optimization - Part I: Nonlinear Ranking Approach," presented at the GECCO 2012, Philadelphia, 2012.
  • [17] C. Floundas and P. Pardalos, A collection of test problems for constrained global optimization, Vol.455. Berlin, Germany: Springer Verlag, 1987.

PROBABILISTIC SORTING FOR EFFECTIVE ELITISM IN MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS

Yıl 2016, Cilt: 1 Sayı: 1, 19 - 27, 30.12.2016

Öz

respect a new approach is presented, which is a probabilistic sorting for effective elitism and ensuing improved and robust convergence. This is achieved by an adaptive probabilistic model representing the commensurate probability density of the random solutions throughout the generations that it yields a probabilistic distance measure which is nonlinear with respect to the range of solutions as to their location in the objectives space. The implementation of the theoretical results leads an effective
evolutionary optimization algorithm accomplished in two stages. In the first stage linear non-dominated sorting, tournament selection and elitism is carried out in objective space. In the second stage, the same is executed in a transformed objective space, where probabilistic distance measure for ranking prevails. The effectiveness of the method is exemplified by a demonstrative computer experiment. The problem treated is selected from the existing literature for comparison, while the experiment carried out and reported here demonstrates the marked performance of the approach. The experiment complies with the theoretical foundations, so that the robust and fast convergence with precision as well as with accuracy is accomplished throughout the search up to 10-10 range or beyond, limited exclusively by machine precision.

Kaynakça

  • [1] C. M. Fonseca, "An overview of evolutionary algorithms in multiobjective optimization," Evolutionary Computation, vol. 3, pp. 1- 16, 1995.
  • [2] C. A. C. Coello, "An updated survey of Ga-based multi-objective optimization techniques," ACM Computing Surveys, vol. 32, pp. 109- 143, 2000.
  • [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] K. Deb and R. Datta, "A bi-objective constrained optimization algorithm using a hybrid evolutionary and penalty function approach," Engineering Optimization, vol. 45, pp. 503-527, 2013.
  • [7] 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.
  • [8] C. A. C. Coello, "Use of a self-adaptive penalty approach for engineering optimization problems," Ciomputers in Industry, vol. 41, pp. 113-127, 2000.
  • [9] S. Gass and T. Saaty, "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, vol. 2, p. 7, 1955. [10] L. Zadeh, "Non-scalar-valued performance criteria," IEEE Trans. Automatic Control, vol. 8, p. 2, 1963. [11] K. Miettinen, Nonlinear Multiobjective Optimization. Boston: Kluwer Academic, 1999.
  • [12] 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.
  • [13] P. L. Meyer, Introductory Probability and Statistical Applications: Addison-Wesley, 1970.
  • [14] G. Bachman and L. Narici, Functional Analysis. New York: Dover, 2000.
  • [15] J. T. Oden and L. F. Demkowicz, Applied Functional Analysis: CRC Press, 1996.
  • [16] O. Ciftcioglu, M. S. Bittermann, and I. S. Sariyildiz, "Precision Evolutionary Optimization - Part I: Nonlinear Ranking Approach," presented at the GECCO 2012, Philadelphia, 2012.
  • [17] C. Floundas and P. Pardalos, A collection of test problems for constrained global optimization, Vol.455. Berlin, Germany: Springer Verlag, 1987.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Articles
Yazarlar

Şahin Serhat Şeker

Michael S. Bittermann

Ramazan Çağlar

Rituparna Datta Bu kişi benim

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

Kaynak Göster

APA Şeker, Ş. S., Bittermann, M. S., Çağlar, R., Datta, R. (2016). PROBABILISTIC SORTING FOR EFFECTIVE ELITISM IN MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS. The Journal of Cognitive Systems, 1(1), 19-27.