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A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization

In: Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems

Author

Listed:
  • Jan-Willem Klinkenberg
  • Michael T. M. Emmerich

    (Leiden University)

  • André H. Deutz
  • Ofer M. Shir
  • Thomas Bäck

Abstract

The SMS-EMOA is a simple and powerful evolutionary metaheuristic for computing approximations to Pareto front based on the dominated hypervolume indicator (S-metric). However, as other state-of-the-art metaheuristics, it consumes a high number of function evaluations in order to compute accurate approximations. To reduce its total computational cost and response time for problems with time consuming evaluators, we suggest three adjustments: Step-size adaptation, Kriging metamodeling, and Steady-State Parallelization. We show that all these measures contribute to the acceleration of the SMS-EMOA on continuous benchmark problems as well as on a application problem – the quantum mechanical optimal control with shaped laser pulses.

Suggested Citation

  • Jan-Willem Klinkenberg & Michael T. M. Emmerich & André H. Deutz & Ofer M. Shir & Thomas Bäck, 2010. "A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization," Lecture Notes in Economics and Mathematical Systems, in: Matthias Ehrgott & Boris Naujoks & Theodor J. Stewart & Jyrki Wallenius (ed.), Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, pages 301-311, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-04045-0_26
    DOI: 10.1007/978-3-642-04045-0_26
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