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hypDE: A Hyper-Heuristic Based on Differential Evolution for Solving Constrained Optimization Problems

  • José Carlos Villela Tinoco
  • Carlos A. Coello Coello
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)

Abstract

In this paper, we present a hyper-heuristic, based on Differential Evolution, for solving constrained optimization problems. Differential Evolution has been found to be a very effective and efficient optimization algorithm for continuous search spaces, which motivated us to adopt it as our search engine for dealing with constrained optimization problems. In our proposed hyper-heuristic, we adopt twelve differential evolution models for our low-level heuristic.We also adopt four selection mechanisms for choosing the low-level heuristic. The proposed approach is validated using a well-known benchmark for constrained evolutionary optimization. Results are compared with respect to those obtained by a state-of-theart constrained differential evolution algorithm (CDE) and another hyper-heuristic that adopts a random descent selection mechanism. Our results indicate that our proposed approach is a viable alternative for dealing with constrained optimization problems.

Keywords

Test Problem Differential Evolution Selection Mechanism Constrain Optimization Problem Differential Evolution Algorithm 
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 2013

Authors and Affiliations

  • José Carlos Villela Tinoco
    • 1
  • Carlos A. Coello Coello
    • 1
  1. 1.Departamento de ComputaciónCINVESTAV-IPN (Evolutionary Computation Group)MéxicoMéxico

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