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Evolutionary Algorithms for Multicriteria Optimization with Selecting a Representative Subset of Pareto Optimal Solutions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)

Abstract

In this paper the method of selecting a representative subset of Pareto optimal solutions is used to make the search of Pareto frontier more effective. Firstly, the evolutionary algorithm method for generating a set of Pareto optimal solutions is described. Then, indiscernibility interval method is applied to select representative subset of Pareto optimal solutions. The main idea of this method consists in removing from the set of Pareto optimal solutions these solutions, which are close to each other in the space of objectives, i.e., those solutions for which the values of the objective functions differ less than an indiscernibility interval. The set of Pareto optimal solutions is reduced using indiscernibility interval method after running a certain number of generations. This process can be called the filtration process in which less important Pareto optimal solutions are removed from the existing set. Finally, two design optimization problems are solved using the proposed method. From these examples it is clear that the computation time can be reduced significantly and still the real Pareto frontier obtained.

Keywords

Evolutionary Algorithm Pareto Optimal Solution Pareto Frontier Filtration Process Pareto Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringCracow University of TechnologyKrakowPoland

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