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The cross-entropy method in multi-objective optimisation: An assessment

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  • Bekker, James
  • Aldrich, Chris

Abstract

Solving multi-objective problems requires the evaluation of two or more conflicting objective functions, which often demands a high amount of computational power. This demand increases rapidly when estimating values for objective functions of dynamic, stochastic problems, since a number of observations are needed for each evaluation set, of which there could be many. Computer simulation applications of real-world optimisations often suffer due to this phenomenon. Evolutionary algorithms are often applied to multi-objective problems. In this article, the cross-entropy method is proposed as an alternative, since it has been proven to converge quickly in the case of single-objective optimisation problems. We adapted the basic cross-entropy method for multi-objective optimisation and applied the proposed algorithm to known test problems. This was followed by an application to a dynamic, stochastic problem where a computer simulation model provides the objective function set. The results show that acceptable results can be obtained while doing relatively few evaluations.

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  • Bekker, James & Aldrich, Chris, 2011. "The cross-entropy method in multi-objective optimisation: An assessment," European Journal of Operational Research, Elsevier, vol. 211(1), pages 112-121, May.
    • Handle: RePEc:eee:ejores:v:211:y:2011:i:1:p:112-121
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    1. Rubinstein, Reuven Y., 1997. "Optimization of computer simulation models with rare events," European Journal of Operational Research, Elsevier, vol. 99(1), pages 89-112, May.
    2. Bettonvil, Bert & del Castillo, Enrique & Kleijnen, Jack P.C., 2009. "Statistical testing of optimality conditions in multiresponse simulation-based optimization," European Journal of Operational Research, Elsevier, vol. 199(2), pages 448-458, December.
    3. Shukla, Pradyumn Kumar & Deb, Kalyanmoy, 2007. "On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1630-1652, September.
    4. Sridhar Bashyam & Michael C. Fu, 1998. "Optimization of (s, S) Inventory Systems with Random Lead Times and a Service Level Constraint," Management Science, INFORMS, vol. 44(12-Part-2), pages 243-256, December.
    5. Beausoleil, Ricardo P., 2006. ""MOSS" multiobjective scatter search applied to non-linear multiple criteria optimization," European Journal of Operational Research, Elsevier, vol. 169(2), pages 426-449, March.
    6. Tsou, Ching-Shih, 2009. "Evolutionary Pareto optimizers for continuous review stochastic inventory systems," European Journal of Operational Research, Elsevier, vol. 195(2), pages 364-371, June.
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    Cited by:

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