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Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems

  • Mario Cámara
  • Julio Ortega
  • Francisco de Toro
Part of the Studies in Computational Intelligence book series (SCI, volume 415)

Abstract

In this chapter some alternatives are discussed to take advantage of parallel computers in dynamic multi-objective optimization problems (DMO) using evolutionary algorithms. In DMO problems, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online. Thus, high performance computing approaches, such as parallel processing, should be applied to these problems to meet the quality requirements within the given time constraints. Taking this into account, we describe two generic parallel frameworks for multi-objective evolutionary algorithms. These frameworks are used to compare the parallel processing performance of some multi-objective optimization evolutionary algorithms: our previously proposed algorithms, SFGA and SFGA2, in conjunction with SPEA2 and NSGA-II.We also propose a model to explain the benefits of parallel processing in multi-objective problems and the speedup results observed in our experiments.

Keywords

dynamic multiobjective optimization (DMO) parallel processing parallel evolutionary algorithms 

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Mario Cámara
    • 1
  • Julio Ortega
    • 1
  • Francisco de Toro
    • 2
  1. 1.Department of Computer Architecture and TechnologyUniversity of GranadaGranadaSpain
  2. 2.Department of Signal Theory, Telematics and CommunicationsUniversity of GranadaGranadaSpain

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