PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Real ant colony optimization as a tool for multi-criteria problems

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a population-based heuristic method – a real ant colony optimization (RACO) as a tool for multi-criteria optimization problems. The idea of multi-criteria optimization is discussed and the necessary modifications of RACO are proposed. These modifications made possible to use the method to simultaneously search many Pareto-optimal solutions. The method was numerically tested in problems of benchmark-type and used for solving simple engineering problems. This article presents and discusses all results obtained in tests, and two different approaches to multi-criteria optimization are additionally compared (search then decision and decision then search).
Rocznik
Strony
255--265
Opis fizyczny
Bibliogr. 14 poz., rys., wykr.
Twórcy
autor
  • Institute of Mathematics Silesian University of Technology Kaszubska 23, 44-100 Gliwice, Poland
autor
  • Institute of Power Engineering and Turbomachinery Silesian University of Technology Konarskiego 18, 44-100 Gliwice, Poland
Bibliografia
  • [1] J. Andersson. Multi-objective Optimization in Engineering Design – Applications to Fluid Power Systems. Dissertation, Linköping Studies in Science and Technology, Dissertation No. 675, Linköping University, Linköping, Sweden, 2001.
  • [2] R. Barron, B. Barron. Designe for Thermal Stressis. Willey&Sons, 2012.
  • [3] U. Diwekar. Introduction to Applied Optimization. Springer, 2008.
  • [4] A. Długosz. Multiobjective evolutionary optimization of MEMS structures. CAMES, 17: 41–50, 2010.
  • [5] E. Elbeltagia, T. Hegazyb, D. Grierso. Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, 19: 43–53, 2005.
  • [6] J. Horn. Multicriteria decision making. [In:] Handbook of Evolutionary Computation, T. Back, D.B. Fogel, Z. Michalewicz [Eds.], F1.9, Institute of Physics Publishing, Bristol(UK), 1997.
  • [7] A. Messac. From dubious construction of objective functions to the application of physical programming, AIAA Journal, 38(1): 155–163, 2000.
  • [8] H. Nakayama. Multi-objective optimization and its engineering applications. [In:] Practical approaches to multiobjective optimization 04461, J. Branke, K. Deb, K. Miettinen, R.E. Steuer [Eds.], Internationales Begegnungsund Forschungszentrum f¨ur Informatik (IBFI), Schloss Dagstuhl, Germany, 2005.
  • [9] V. Pareto, Manuale di Economia Politica, Societa Editrice Libraria, Milano, Italy, 1906. Translated into English by A.S. Schwier as Manual of Political Economy, Macmillan, New York, 1971.
  • [10] K. Socha, M. Dorigo. Ant colony optimization for continuous domains, European Journal of Operational Research, 185: 1155–1173, 2008.
  • [11] O.L. de Weck. Multiobjective optimization: history and promise. Keynote paper. The 3rd China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems, Kanazawa, Japan, October 30 – November 2, 2004.
  • [12] Z. Wenping, Z. Yunlong, Ch. Hanning, Z. Beiwei. Solving multiobjective optimization problems using artificial bee colony algorithm. Discrete Dynamics in Nature and Society, article ID 569784: 1–37, 2011.
  • [13] E. Zitzler. Evolutionary Algorithms for Multiobjective Optimisation: Methods and Applications. PhD thesis, Eidgenössische Technische Hochschule Zürich, 1999.
  • [14] E. Zitzler, K. Deb, L. Thiele. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 8(2): 173–195, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b99116bc-d316-4c68-9f37-216af2166809
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.