Applications of Parallel Platforms and Models in Evolutionary Multi-Objective Optimization

  • Antonio López Jaimes
  • Carlos A. Coello Coello
Part of the Studies in Computational Intelligence book series (SCI, volume 210)


This chapter presents a review of modern parallel platforms and the way in which they can be exploited to implement parallel multi-objective evolutionary algorithms. Regarding parallel platforms, a special emphasis is given to global metacomputing which is an emerging form of parallel computing with promising applications in evolutionary (both multi- and singleobjective) optimization. In addition, we present the well-known models to parallelize evolutionary algorithms (i.e., master-slave, island, diffusion and hybrid models) describing some possible strategies to incorporate these models in the context of multi-objective optimization. Since an important concern in parallel computing is performance assessment, the chapter also presents how to apply parallel performance measures in multi-objective evolutionary algorithms taking into consideration their stochastic nature. Finally, we present a selection of current parallel multi-objective evolutionary algorithms that integrate novel strategies to address multi-objective issues.


Pareto Front Island Model Multiobjective Evolutionary Algorithm True Pareto Front Volunteer Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Antonio López Jaimes
    • 1
  • Carlos A. Coello Coello
    • 1
  1. 1.Departamento de ComputaciónCINVESTAV-IPN (Evolutionary Computation Group)MéxicoMéxico

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