Parallel Evolutionary Multiobjective Optimization

  • Francisco Luna
  • Antonio J. Nebro
  • Enrique Alba
Part of the Studies in Computational Intelligence book series (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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Copyright information

© Springer 2006

Authors and Affiliations

  • Francisco Luna
    • 1
  • Antonio J. Nebro
    • 1
  • Enrique Alba
    • 1
  1. 1.Dpto. Lenguajes y Ciencias de la ComputaciónE.T.S.I. Informática University of MálagaSpain

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