Computing the Most Significant Solution from Pareto Front obtained in Multi-objective Evolutionary

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 lar...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2010-01, Vol.1 (4)
Main Authors: Chaudhari, P.M, R.V., Dr, V., Dr
Format: Article
Language:English
Subjects:
Clustering
Decision support systems
Evolutionary algorithms
Multiple objective analysis
Optimization techniques
Pareto optimization
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2010.010411