Using a Family of Curves to Approximate the Pareto Front of a Multi-Objective Optimization Problem View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Saúl Zapotecas Martínez , Víctor A. Sosa Hernández , Hernán Aguirre , Kiyoshi Tanaka , Carlos A. Coello Coello

ABSTRACT

The design of selection mechanisms based on quality assessment indicators has become one of the main research topics in the development of Multi-Objective Evolutionary Algorithms (MOEAs). Currently, most indicator-based MOEAs have employed the hypervolume indicator as their selection mechanism in the search process. However, hypervolume-based MOEAs become inefficient (and eventually, unaffordable) as the number of objectives increases. In this paper, we study the construction of a reference set from a family of curves. Such reference set is used together with a performance indicator (namely Δ p ) to assess the quality of solutions in the evolutionary process of an MOEA. We show that our proposed approach is able to deal (in an efficient way) with problems having many objectives (up to ten objective functions). Our preliminary results indicate that our proposed approach is highly competitive with respect to two state-of-the-art MOEAs over the set of test problems that were adopted in our comparative study. More... »

PAGES

682-691

References to SciGraph publications

  • 2005. Scalable Test Problems for Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
  • 2005-06. Solving Multiobjective Optimization Problems Using an Artificial Immune System in GENETIC PROGRAMMING AND EVOLVABLE MACHINES
  • 2004. Indicator-Based Selection in Multiobjective Search in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • Book

    TITLE

    Parallel Problem Solving from Nature – PPSN XIII

    ISBN

    978-3-319-10761-5
    978-3-319-10762-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-10762-2_67

    DOI

    http://dx.doi.org/10.1007/978-3-319-10762-2_67

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1014716259


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