Problems with multiple objectives can be solved by using Pareto optimization techniques in evolutionary multi-objective optimization algorithms. Many applications involve multiple objective functions and the Pareto front may contain a very large number of points. Selecting a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. Implementation of this methodology for various applications and in a decision support system is also discussed.
%0 Journal Article
%1 IJACSA.2010.010411
%A P.M Chaudhari Dr. R.V. Dharaskar, Dr. V M Thakare
%D 2010
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Clustering front techniques,Multiobjective,Pareto
%N 4
%T Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary
%U http://ijacsa.thesai.org/
%V 1
%X Problems with multiple objectives can be solved by using Pareto optimization techniques in evolutionary multi-objective optimization algorithms. Many applications involve multiple objective functions and the Pareto front may contain a very large number of points. Selecting a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. Implementation of this methodology for various applications and in a decision support system is also discussed.
@article{IJACSA.2010.010411,
abstract = {Problems with multiple objectives can be solved by using Pareto optimization techniques in evolutionary multi-objective optimization algorithms. Many applications involve multiple objective functions and the Pareto front may contain a very large number of points. Selecting a solution from such a large set is potentially intractable for a decision maker. Previous approaches to this problem aimed to find a representative subset of the solution set. Clustering techniques can be used to organize and classify the solutions. Implementation of this methodology for various applications and in a decision support system is also discussed.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{P.M Chaudhari Dr. R.V. Dharaskar}, Dr. V M Thakare},
biburl = {https://www.bibsonomy.org/bibtex/22b7339510f37c58f2b48862949c9af62/thesaiorg},
interhash = {7149085e7aed3bce48d12d5954282c0f},
intrahash = {2b7339510f37c58f2b48862949c9af62},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Clustering front techniques,Multiobjective,Pareto},
number = 4,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary}},
url = {http://ijacsa.thesai.org/},
volume = 1,
year = 2010
}