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A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm

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  • Baquela, Enrique Gabriel
  • Olivera, Ana Carolina

Abstract

Optimization via Simulation (OvS) is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically. OvS consists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objective OvS algorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popular NSGA-II. The hybrid method is compared to the canonical NSGA-II and other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.

Suggested Citation

  • Baquela, Enrique Gabriel & Olivera, Ana Carolina, 2019. "A novel hybrid multi-objective metamodel-based evolutionary optimization algorithm," Operations Research Perspectives, Elsevier, vol. 6(C).
    • Handle: RePEc:eee:oprepe:v:6:y:2019:i:c:s221471601830068x
    DOI: 10.1016/j.orp.2019.100098
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    References listed on IDEAS

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    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    2. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    3. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, December.
    4. Joan Davins-Valldaura & Saïd Moussaoui & Guillermo Pita-Gil & Franck Plestan, 2017. "ParEGO extensions for multi-objective optimization of expensive evaluation functions," Journal of Global Optimization, Springer, vol. 67(1), pages 79-96, January.
    5. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
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