An Alternative Preference Relation to Deal with Many-Objective Optimization Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Antonio L’opez , Carlos A. Coello Coello , Akira Oyama , Kozo Fujii

ABSTRACT

In this paper, we use an alternative preference relation that couples an achievement function and the ε-indicator in order to improve the scalability of a Multi-Objective Evolutionary Algorithm (moea) in many-objective optimization problems. The resulting algorithm was assessed using the Deb-Thiele-Laumanns-Zitzler (dtlz) and the Walking- Fish-Group (wfg) test suites. Our experimental results indicate that our proposed approach has a good performance even when using a high number of objectives. Regarding the dtlz test problems, their main difficulty was found to lie on the presence of dominance resistant solutions. In contrast, the hardness of wfg problems was not found to be significantly increased by adding more objectives. More... »

PAGES

291-306

References to SciGraph publications

  • 2007. Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2003. The Maximin Fitness Function; Multi-objective City and Regional Planning in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 1999. Multi-objective Optimization in Evolutionary Algorithms Using Satisfiability Classes in COMPUTATIONAL INTELLIGENCE
  • 2007. Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2007. Quantifying the Effects of Objective Space Dimension in Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2011. Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization in LEARNING AND INTELLIGENT OPTIMIZATION
  • 1980. The Use of Reference Objectives in Multiobjective Optimization in MULTIPLE CRITERIA DECISION MAKING THEORY AND APPLICATION
  • 2005. A Scalable Multi-objective Test Problem Toolkit in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2004. Indicator-Based Selection in Multiobjective Search in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • Book

    TITLE

    Evolutionary Multi-Criterion Optimization

    ISBN

    978-3-642-37139-4
    978-3-642-37140-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-37140-0_24

    DOI

    http://dx.doi.org/10.1007/978-3-642-37140-0_24

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018617843


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