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Preference Incorporation into Evolutionary Multiobjective Optimization Using a Multi-Criteria Evaluation Method

  • Laura Cruz-Reyes
  • Eduardo Fernandez
  • Claudia Gomez
  • Patricia Sanchez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

Most approaches in the evolutionary multiobjective optimization literature concentrate mainly on generating an approximation of the Pareto front. However, this does not completely solve the problem since the Decision Maker (DM) still has to choose the best compromise solution out of that set. This task becomes difficult when the number of criteria increases. In this chapter, we introduce a new way to incorporate and update the DM’s preferences into a Multiobjective Evolutionary Algorithm, expressed in a set of solutions assigned to ordered categories. We propose a variant of the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-MultiCriteria Sorting Genetic Algorithm (H-MCSGA). In this algorithm, we strengthen the selective pressure based on dominance adding selective pressure based on assignments to categories. Particularly, we make selective pressure towards non-dominated solutions that belong to the best category. In instances with 9 objectives on the project portfolio problem, H-MCSGA outperforms NSGA-II obtaining non-dominated solutions that belong to the most preferred category.

Keywords

Pareto Front Pareto Optimal Solution Pareto Frontier Multiobjective Optimization Problem Multiobjective Evolutionary Algorithm 
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.

Notes

Acknowledgments

This work was partially financed by CONACYT, PROMEP and DGEST.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Laura Cruz-Reyes
    • 1
  • Eduardo Fernandez
    • 2
  • Claudia Gomez
    • 1
  • Patricia Sanchez
    • 1
  1. 1.Instituto Tecnologico de Ciudad MaderoTamaulipasMexico
  2. 2.Universidad Autonoma de SinaloaSinaloaMexico

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