CFP last date
20 June 2024
Reseach Article

Post Pareto Analysis in Multi-objective Optimization

Published on December 2013 by P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare
National Conference on Innovative Paradigms in Engineering & Technology 2013
Foundation of Computer Science USA
NCIPET2013 - Number 4
December 2013
Authors: P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare
632dc817-ead4-4307-80c7-b677e9936533

P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare . Post Pareto Analysis in Multi-objective Optimization. National Conference on Innovative Paradigms in Engineering & Technology 2013. NCIPET2013, 4 (December 2013), 15-18.

@article{
author = { P. M. Chaudhari, R. V. Dharaskar, V. M. Thakare },
title = { Post Pareto Analysis in Multi-objective Optimization },
journal = { National Conference on Innovative Paradigms in Engineering & Technology 2013 },
issue_date = { December 2013 },
volume = { NCIPET2013 },
number = { 4 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/ncipet2013/number4/14719-1355/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Innovative Paradigms in Engineering & Technology 2013
%A P. M. Chaudhari
%A R. V. Dharaskar
%A V. M. Thakare
%T Post Pareto Analysis in Multi-objective Optimization
%J National Conference on Innovative Paradigms in Engineering & Technology 2013
%@ 0975-8887
%V NCIPET2013
%N 4
%P 15-18
%D 2013
%I International Journal of Computer Applications
Abstract

The proposed methodology is based on efficient clustering technique for facilitating the decision-maker in the analysis of the solutions of multi-objective problems . Choosing a solution for system implementation from the Pareto-optimal set can be a difficult task, generally because Pareto-optimal sets can be extremely large or even contain an infinite number of solutions. The proposed technique provides the decision-maker a smaller set of optimal tradeoffs.

References
  1. Hirotaka Nakayama, Multi-objective Optimization and its Engineering Applications, Proc. of third China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical System, pp. 13-26 (2004)
  2. Zitzler, E. and Thiele, L. (1999), Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, p. 257–271.
  3. Kaufman L. and Rousseeuw P. J. (1990). Finding groups in data. An introduction to Cluster Analysis. Wiley-Interscience.
  4. Kalyanmoy Deb and Shamik Chaudhuri. I-MODE: An Interactive Multi-Objective Optimization and Decision-Making using Evolutionary Methods. KanGAL Report Number 2007003.
  5. A. Rakhlin. Stability of clustering methods. NIPS Workshop"Theoretical Foundations of Clustering", December 2005.
  6. Guha, S. , R. Rastogi & K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proc. Of ACM SIGMOD Intl. Conf. on Management of Data, pp. 73-82, 1998.
  7. Deb, K. , Agarwal, S. , Pratap, A. and Meyarivan, T. (2000a). A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL Report Number 200001, Indian Institute of Technology. Kanpur, India.
  8. Davidson, I. , Ravi, S. S. : Clustering with constraints: Feasibility issues and the k-means algorithm. In: Proceedings of the 2005 SIAM International Conference on Data Mining. (2005)
  9. Kaufman, L. and P. J. Rousseeuw, 1990. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
  10. Karypis G. , Han E. -H. and Kumar V. (1999) Chameleon: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 32(8) pp68-75.
  11. P. S. Bradley, U. Fayyad, and C. Reina, "Scaling Clustering Algorithms to Large Databases", To appear,Proc. 4th International Conf. on Knowledge Discovery and Data Mining (KDD-98). AAAI Press, Aug. 1998.
  12. Rousseeuw P. J. (1987): Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53- 65.
  13. Rousseeuw P. , Trauwaert E. an d Kaufman L. (1989): Some silhouette-based graphics for clustering interpretation. Belgian Journal of Operations Research, Statistics and Computer Science, 29(3).
  14. Pradyumn Kumar Shukla and Kalyanmoy Deb. On Finding Multiple Pareto-Optimal Solutions Using Classical and Evolutionary Generating Methods. KanGAL Report Number 2005006
  15. A tabu method to find the Pareto solutions of multiobjectiveoptimal design problems in electromagnetics Ho, S. L. ; Shiyou Yang; Guangzheng Ni; Wong, H. C. Magnetics, IEEE Transactions on Volume 38, Issue 2, Mar 2002 Page(s):1013 – 1016 Digital Object Identifier 10. 1109/20. 996260
  16. Venkat V. , Jacobson S. and Stori J. (2004). A post-Optimality Analysis Algorithm for Multi-Objective Optimization. Computational Optimization and Applications, 28, 357- 372. [17 ] Chankong, V. and Haimes, Y. (1983). Multiobjective decision making theory and methodology. New York:North-Holland.
  17. Jasper A. Vrugt and Bruce A. Robinson. (2007). Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci U S A. 2007 January 16; 104(3): 708–711.
  18. Jose Maria A. Pangilinan, and Gerrit K. Janssens. Evolutionary Algorithms for the Multiobjective Shortest Path Problem. World Academy of Science, Engineering and Technology 25 2007
  19. Kalyanmoy Deb. Advances in evolutionary computing: theory and applications Pages: 263 - 292 Year of Publication: 2003 ISBN:3-540-43330-9
  20. A. F. Gomez-Skarmeta, M. Delgado, M. A. Vila. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets and Systems, 1999, 106(2):179-188.
  21. P. M. Chaudhari, R. V. Dharaskar , V. M. Thakare (2010): " Computing the most significant solution from pareto front obtained in multi objective algorithms. ", (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 1, No. 4, October 2010:63-68
Index Terms

Computer Science
Information Sciences

Keywords

Multi-objective Problems Pareto-optimal Sets Clustering Technique