An alternative hypervolume-based selection mechanism for multi-objective evolutionary algorithms View Full Text


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Article Info

DATE

2017-02

AUTHORS

Adriana Menchaca-Mendez, Carlos A. Coello Coello

ABSTRACT

In this paper, we are interested in selection mechanisms based on the hypervolume indicator with a particular emphasis on the mechanism used in an improved version of the S metric selection evolutionary multi-objective algorithm (SMS-EMOA) called iSMS-EMOA, which exploits the locality property of the hypervolume. Here, we propose a new selection scheme which approximates the contribution of solutions to the hypervolume and it is designed to preserve the locality property exploited by iSMS-EMOA. This approach is proposed as an alternative to the use of exact hypervolume calculations and is aimed for solving many-objective optimization problems. The proposed approach is called “approximate version of the improved SMS-EMOA (aviSMS-EMOA)” and is validated using standard test problems (with three or more objectives) and performance indicators taken from the specialized literature. Our preliminary results indicate that our proposed approach is a good alternative to solve many-objective optimization problems, if we consider both quality in the solutions and running time required to obtain them because it outperforms two versions of the original SMS-EMOA that approximate the contributions to the hypervolume, it outperforms MOEA/D using penalty boundary intersection and it is competitive with respect to the original SMS-EMOA in several of the test problems adopted. Also, its computational cost is reasonable (it is slower than MOEA/D, but faster than SMS-EMOA). More... »

PAGES

861-884

References to SciGraph publications

  • 2005. Scalable Test Problems for Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
  • 2010. Theoretically Investigating Optimal μ-Distributions for the Hypervolume Indicator: First Results for Three Objectives in PARALLEL PROBLEM SOLVING FROM NATURE, PPSN XI
  • 2011. Computing Hypervolume Contributions in Low Dimensions: Asymptotically Optimal Algorithm and Complexity Results in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2003. The Measure of Pareto Optima Applications to Multi-objective Metaheuristics in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2014. Empirical Performance of the Approximation of the Least Hypervolume Contributor in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XIII
  • 1998. Multiobjective optimization using evolutionary algorithms — A comparative case study in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN V
  • 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
  • 2005. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00500-015-1819-x

    DOI

    http://dx.doi.org/10.1007/s00500-015-1819-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1037872233


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