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Scalable Test Problems for Evolutionary Multiobjective Optimization

  • Kalyanmoy Deb
  • Lothar Thiele
  • Marco Laumanns
  • Eckart Zitzler
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Summary

After adequately demonstrating the ability to solve different two-objective optimization problems, multiobjective evolutionary algorithms (MOEAs) must demonstrate their efficacy in handling problems having more than two objectives. In this study, we have suggested three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of the shape and the location of the resulting Pareto-optimal front, and introduction of controlled difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of the above features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and better understanding of the working principles of MOEAs.

Keywords

Test Problem Multiobjective Optimization Objective Space Multiobjective Evolutionary Algorithm Feasible Search Space 
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 London Limited 2005

Authors and Affiliations

  • Kalyanmoy Deb
  • Lothar Thiele
  • Marco Laumanns
  • Eckart Zitzler

There are no affiliations available

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