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MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9018)

Abstract

Hyper-Heuristics is a high-level methodology for selection or automatic generation of heuristics for solving complex problems. Despite the hyper-heuristics success, there is still only a few multi-objective hyper-heuristics. Our approach, MOEA/D-HH, is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. It uses an innovative adaptive choice function proposed in this work to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied to each individual during a MOEA/D execution. We tested MOEA/D-HH in a well established set of 10 instances from the CEC 2009 MOEA Competition. MOEA/D-HH is compared with some important multi-objective optimization algorithms and the resultsobtained are promising.

Keywords

  • Hyper-heuristic
  • Choice function
  • MOEA/D

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Correspondence to Richard A. Gonçalves .

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Gonçalves, R.A., Kuk, J.N., Almeida, C.P., Venske, S.M. (2015). MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-15934-8_7

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