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Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems

Abstract

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.

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Category:
Monographic publication
Type:
rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:
Advanced Solutions in Diagnostics and Fault Tolerant Control strony 203 - 214
ISSN:
2194-5357
Language:
English
Publication year:
2018
Bibliographic description:
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): , 2018, s.203-214
DOI:
Digital Object Identifier (open in new tab) 10.1007/978-3-319-64474-5_17
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  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) open in new tab
  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) open in new tab
  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 open in new tab
  5. Deb, K.: Current Trends in Evolutionary Multi-objective Optimization. Interna- tional Journal for Simulation and Multidisciplinary Optimisation, 1(1), pp. 18, (2007) open in new tab
  6. Deb, K., Gupta, H.: Introducing robustness in multi-objective optimization. Evolu- tionary Computation Journal, 14(4), pp. 463494, (2006) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  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 open in new tab
  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 open in new tab
  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 open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  28. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, (1996) open in new tab
  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) open in new tab
  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) open in new tab
  34. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, 2(3), pp. 221-248, (1994) open in new tab
  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) open in new tab
  36. Song Goh, K., Lim, A., Rodrigues, B.: Sexual selection for genetic algorithms, Artificial Intelligence Review, pp. 123-152, (2003) open in new tab
  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) open in new tab
  38. Vrajitoru, D.: Simulating Gender Separation with Genetic Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 634-641, (2002) open in new tab
  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) open in new tab
  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) open in new tab
  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) open in new tab
  43. Zitzler, E., Thiele, L., Bader, J.: On set-based multiobjective optimization. IEEE Transactions on Evolutionary Computation, 14(1), pp. 58 -79, (2010) open in new tab
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