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Performance of Multiple Objective Evolutionary Algorithms on a Distribution System Design Problem - Computational Experiment

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

Abstract

The paper presents a comparative experiment with four multiple objective evolutionary algorithms on a real life combinatorial optimization problem. The test problem corresponds to the design of a distribution system. The experiment compares performance of a Pareto ranking based multiple objective genetic algorithm (Pareto GA), multiple objective multiple start local search (MOMSLS), multiple objective genetic local search (MOGLS) and an extension of Pareto GA involving local search (Pareto GLS). The results of the experiment clearly indicate that the methods hybridizing recombination and local search operators by far outperform methods that use one of the operators alone. Furthermore, MOGLS outperforms Pareto GLS.

Keywords

Local Search Distribution Center Temporary Population Travelling Salesperson Problem Local Search Operator 
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.Institute of Computing SciencePoznań University of Technology ulPoznańpoland

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