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.

P.M Chaudhari, Dr. R.V. Dharaskar and Dr. V. M. Thakare, “Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary” International Journal of Advanced Computer Science and Applications(IJACSA), 1(4), 2010. http://dx.doi.org/10.14569/IJACSA.2010.010411

@article{Chaudhari2010,
title = {Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2010.010411},
url = {http://dx.doi.org/10.14569/IJACSA.2010.010411},
year = {2010},
publisher = {The Science and Information Organization},
volume = {1},
number = {4},
author = {P.M Chaudhari and Dr. R.V. Dharaskar and Dr. V. M. Thakare}
}