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Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems

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Parallel Architectures and Bioinspired Algorithms

Part of the book series: Studies in Computational Intelligence ((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.

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Authors and Affiliations

  1. Department of Computer Architecture and Technology, University of Granada, Granada, Spain

    Mario Cámara & Julio Ortega

  2. Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain

    Francisco de Toro

Authors
  1. Mario Cámara
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  2. Julio Ortega
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  3. Francisco de Toro
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Correspondence to Mario Cámara .

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Editors and Affiliations

  1. Centro Universitario de Mérida, Universidad de Extremadura, C/ Sta Teresa de Jornet 38, Mérida, 06800, Spain

    Francisco Fernández de Vega

  2. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    José Ignacio Hidalgo Pérez

  3. Facultad de Informática, Universidad Complutense de Madrid, Calle del Profesor José García santesmases s/n, Madrid, 28040, Spain

    Juan Lanchares

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Cámara, M., Ortega, J., de Toro, F. (2012). Comparison of Frameworks for Parallel Multiobjective Evolutionary Optimization in Dynamic Problems. In: Fernández de Vega, F., Hidalgo Pérez, J., Lanchares, J. (eds) Parallel Architectures and Bioinspired Algorithms. Studies in Computational Intelligence, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28789-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-28789-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28788-6

  • Online ISBN: 978-3-642-28789-3

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