Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems - Publikacja - MOST Wiedzy

Wyszukiwarka

Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems

Abstrakt

This paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria, such as those based on the true Pareto front, are difficult to calculate. Whereas, on the other hand, the proposed approximated quality criteria are easy to implement, computationally inexpensive, and sufficiently effective.

Cytowania

  • 0

    CrossRef

  • 1

    Web of Science

  • 1

    Scopus

Pełna treść

pobierz publikację
pobrano 18 razy

Licencja

Copyright (Springer International Publishing AG 2018)

Informacje szczegółowe

Kategoria:
Aktywność konferencyjna
Typ:
publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Tytuł wydania:
Advanced Solutions in Diagnostics and Fault Tolerant Control strony 203 - 214
ISSN:
2194-5357
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
Kowalczuk Z., Białaszewski T.: Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems// Advanced Solutions in Diagnostics and Fault Tolerant Control/ ed. J. Kacprzyk Cham (Switzerland): Springer International Publishing AG, 2018 , 2018, s.203-214
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/978-3-319-64474-5_17
Bibliografia: test
  1. Bader, J., Zitzler, E.: A Hypervolume-Based Optimizer for High-Dimensional Ob- jective Spaces. Conference on Multiple Objective and Goal Programming (MOPGP 2008), Lecture Notes in Economics and Mathematical Systems, Springer (2009) otwiera się w nowej karcie
  2. Bia laszewski, T., Kowalczuk, Z.: Solving highly-dimensional multi-objective opti- mization problems by means of genetic gender. Advanced and Intelligent Compu- tations in Diagnosis and Control. Advances in Intelligent Systems and Computing, pp. 317-329, Springer-Verlag, ChamNew YorkLondon, AISC 386, (2016) otwiera się w nowej karcie
  3. Coello, C.C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary algorithms for solving multi-objective problems. Genetic and Evolutionary Computation, (2nd edi- tion), Springer, Berlin (2007)
  4. Cotta, C., Schaefer, R.: Special Issue on Evolutionary Computation. International Journal otwiera się w nowej karcie
  5. Deb, K.: Current Trends in Evolutionary Multi-objective Optimization. Interna- tional Journal for Simulation and Multidisciplinary Optimisation, 1(1), pp. 18, (2007) otwiera się w nowej karcie
  6. Deb, K., Gupta, H.: Introducing robustness in multi-objective optimization. Evolu- tionary Computation Journal, 14(4), pp. 463494, (2006) otwiera się w nowej karcie
  7. Deb, K., Mohan, M., Mishra, S.: Evaluating the domination-based multiobjective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evolu- tionary Computation Journal, 13(4), pp. 501525, (2005) otwiera się w nowej karcie
  8. Emmerich, M., Beume, N., Naujoks, B.: An EMO algorithm using the hypervolume measure as selection criterion. Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, 3410, pp 62-76, Springer Berlin Heidelberg,(2005) otwiera się w nowej karcie
  9. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiob- jective optimization. IEEE Trans. Evolutionary Computation, 3(1), pp. 1-16, (1995) otwiera się w nowej karcie
  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learn- ing. Addison-Wesley, Reading, MA, (1989)
  11. Hajela, P., Lin, C.Y.: Genetic search strategies in multicriterion optimal design. Structural Optimization, 4, pp. 99-107, (1992) otwiera się w nowej karcie
  12. Holland, H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, (1975)
  13. Horn, J., Nafpliotis, N.: Multiobjective optimization using the niched Pareto ge- netic algorithm. Technical Report, (93005). Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana, Champaign, (1993)
  14. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. IEEE World Congress on Computational Computation, 1, pp. 82-87, Piscataway, NJ, (1994) otwiera się w nowej karcie
  15. Korbicz, J., Kocielny, J.M., Kowalczuk, Z., Cholewa, W. (Eds.): Fault Diagnosis, Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin, (2004)
  16. Kowalczuk, Z., Bia laszewski, T.: Improving evolutionary multi-objective optimi- sation by niching. International Journal of Information Technology and Intelligent Computing, 1(2), pp. 245-257, (2006) otwiera się w nowej karcie
  17. Kowalczuk, Z., Bia laszewski, T.: Improving evolutionary multi-objective optimi- sation using genders. Artificial Intelligence and Soft Computing, Lecture Notes in Artificial Intelligence, 4029, pp. 390-399. SpringerVerlag, Berlin, (2006) otwiera się w nowej karcie
  18. Kowalczuk, Z., Bia laszewski, T.: Designing FDI observers by improved evolution- ary multi-objective optimization. Proc. 6th IFAC Symposium on Fault Detection, Supervision and Safety for Tech. Processes, pp. 601-606, Beijing, China, (2006) otwiera się w nowej karcie
  19. Kowalczuk, Z. and Bia laszewski, T.: Niching mechanisms in evolutionary computa- tions. Int. Journal of Applied Mathematics and Computer Science, 16(1), pp. 59-84, (2006)
  20. Kowalczuk, Z., Bia laszewski, T.: Gender selection of a criteria structure in multi- objective optimization of decision systems (in Polish). Pomiary Automatyka Kon- trola, 57(7), pp. 810-814, (2011) otwiera się w nowej karcie
  21. Kowalczuk, Z., Bia laszewski, T.: Gender approach to multi-objective optimiza- tion of detection systems by pre-selection of criteria. Intelligent Systems in Tech- nical and Medical Diagnosis. Advances in Intelligent Systems and Computing. Springer-Verlag, AISC 230, pp. 161-174, Berlin Heidelberg (2013). doi:10.1007/ 978-3-642-39881-0 13 otwiera się w nowej karcie
  22. Kowalczuk, Z., Bia laszewski, T.: Solving highly-dimensional multi-objective opti- mization problems by means of genetic gender. Advanced and Intelligent Compu- tations in Diagnosis and Control, Advances in Intelligent Systems and Computing.
  23. Springer IP Switzerland, AISC 386, pp. 317 -329, Cham Heidelberg New York Dordrecht London (2016). doi:10.1007/978-3-319-23180-8 23 otwiera się w nowej karcie
  24. Kowalczuk, Z. and Bia laszewski, T.: Gender approaches to evolutionary multi- objective optimization using pre-selection of criteria. Engineering Optimization, Taylor and Francis (2017). doi.org/10.1080/0305215X.2017.1305374 otwiera się w nowej karcie
  25. Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized dif- ferential evolution. IEEE Congress on Evolutionary Computation, vol. 1, 443-450, (2005) otwiera się w nowej karcie
  26. Lis, J., Eiben, A.: A multi-sexual genetic algorithm for multiobjective optimization. Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 59-64, (1997) otwiera się w nowej karcie
  27. Liu, B., Fernndez, F.V., Zhang, Q., Pak, M., Sipahi, S., Gielen G.G.E.: An en- hanced MOEA/D-DE and its application to multiobjective analog cell sizing. IEEE Congress on Evolutionary Computation, pp. 1-7, (2010) otwiera się w nowej karcie
  28. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, (1996) otwiera się w nowej karcie
  29. Qingfu Z., Aimin Z., Shizheng Z., Ponnuthurai N. S., Wudong L., Santosh T.: Multiobjective optimization test instances for the CEC 2009 Special Session and Competition. Working Report, CES-887, School of Computer Science and Electrical Engineering, University of Essex, (2009)
  30. Rejeb, J., AbuElhaija, M.: New gender genetic algorithm for solving graph parti- tioning problems. Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems, 1, pp. 444-446, (2000) otwiera się w nowej karcie
  31. Sanchez-Velazco, J., Bullinaria, J.A.: Gendered selection strategies in genetic al- gorithms for optimization. Proceedings of the UK Workshop on Computational Intelligence, pp. 217-223, Bristol, UK, (2003)
  32. Sanchez-Velazco, J., Bullinaria, J.A.: Sexual Selection with Competitive/Co- Operative Operators for Genetic Algorithms. Proc. the IASTED Intern. Conf. on Neural Networks and Computational Intelligence. ACTA Press, pp. 191-196, (2003)
  33. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic al- gorithms, Proc. Intern. Conf. on Genetic Algorithms and their Applications, pp. 93-100. Lawrence Erlbaum Associates, Pittsburgh, PA, (1985) otwiera się w nowej karcie
  34. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2(3), pp. 221-248, (1994) otwiera się w nowej karcie
  35. Sodsee, S., Meesad, P., Li, Z., Halang, W.: A networking requirement applica- tion by multi-objective genetic algorithms with sexual selection. 3rd International Conference Intelligent System and Knowledge Engineering, 1, pp. 513-518, (2008) otwiera się w nowej karcie
  36. Song Goh, K., Lim, A., Rodrigues, B.: Sexual selection for genetic algorithms, Artificial Intelligence Review, pp. 123-152, (2003) otwiera się w nowej karcie
  37. Viennet, R., Fontiex, C., Marc, I.: Multicriteria optimisation using a genetic algo- rithm for determining a Pareto set. International Journal of Systems Science, 27(2), pp. 255-260, (1996) otwiera się w nowej karcie
  38. Vrajitoru, D.: Simulating Gender Separation with Genetic Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 634-641, (2002) otwiera się w nowej karcie
  39. While L., Hingston P., Barone L., Huband S.: A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation, 10(1), pp 29-38, (2006) otwiera się w nowej karcie
  40. Yan, T.: An improved genetic algorithm and its blending application with neural network. 2nd International Workshop Intelligent Systems and Applications, pp. 1-4, (2010) otwiera się w nowej karcie
  41. Zhang, Q., Li H.: MOEA/D: A multi-objective evolutionary algorithm based on de- composition, IEEE Trans. on Evolutionary Computation, 11(6), pp. 712-731, (2007)
  42. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Com- putation, 3(4), pp. 257 -271, (1999) otwiera się w nowej karcie
  43. Zitzler, E., Thiele, L., Bader, J.: On set-based multiobjective optimization. IEEE Transactions on Evolutionary Computation, 14(1), pp. 58 -79, (2010) otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 44 razy

Publikacje, które mogą cię zainteresować

Meta Tagi