Applying Clustering Technique in Organizing, Classifying and Finding the Most Significant Solution from the Large Result Set obtained in Multi-Objective Evolutionary Algorithms

Abstract

Multiobjective optimization problems can be solved using Pareto Optimization techniques including evolutionary multiobjective optimization algorithms. Many real world applications involve multiple objective functions and the Pareto front many contain a very large number of points. Choosing 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. A Evolutionary algorithm-based k-means clustering technique is proposed in this paper. The searching capability of Evolutionary algorithms is exploited in order to search for appropriate cluster centres in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centres of a fixed number of clusters. Applicability of this methodology for various applications and in a decision support system is also discussed.