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Parallel Evolutionary Multiobjective Optimization

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Parallel Evolutionary Computations

Part of the book series: Studies in Computational Intelligence ((SCI,volume 22))

Abstract

Research on multiobjective optimization is very active currently because most of the real-world engineering optimization problems are multiobjective in nature. Multiobjective optimization does not restrict to find a unique single solution, but a set of solutions collectively known as the Pareto front. Evolutionary algorithms (EAs) are especially well-suited for solving such kind of problems because they are able to find multiple trade-off solutions in a single run. However, these algorithms may be computationally expensive because (1) real-world problem optimization typically involves tasks demanding high computational resources and (2) they are aimed at finding the whole front of optimal solutions instead of searching for a single optimum. Parallelizing EAs arises as a possible way of facing this drawback. The first goal of this chapter is to provide the reader with a wide overview of the literature on parallel EAs for multiobjective optimization. Later, we include an experimental study where we develop and analyze pPAES, a parallel EA for multiobjective optimization based on the Pareto Archived Evolution Strategy (PAES). The obtained results show that pPAES is a promising option for solving multiobjective optimization problems.

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  1. Dpto. Lenguajes y Ciencias de la Computación, E.T.S.I. Informática University of Málaga, Campus de Teatinos 29071 Málaga, Spain

    Francisco Luna, Antonio J. Nebro & Enrique Alba

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  1. Department of System Engineering and Computation, Faculty of Engineering State University of Rio de Janeiro Rua São Francisco Xavier, 524, 5o. andar Maracanã CEP 20559-900, Rio de Janeiro, RJ, Brazil

    Nadia Nedjah Dr.

  2. Department of System Engineering and Computation, Faculty of Engineering State University of Rio de Janeiro Rua São Francisco Xavier, 524, 5o. andar Maracanã CEP 20559-900, Rio de Janeiro, RJ, Brazil

    Luiza de Macedo Mourelle Dr.

  3. Depto. of Lenguajes y Ciencias de la Computación Campus de Teatinos, Universidad de Málaga, Málaga, 29071, Spain

    Enrique Alba Professor

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Luna, F., Nebro, A.J., Alba, E. (2006). Parallel Evolutionary Multiobjective Optimization. In: Nedjah, N., Mourelle, L.d., Alba, E. (eds) Parallel Evolutionary Computations. Studies in Computational Intelligence, vol 22. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-32839-4_2

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