Efficient hybrid methods for global continuous optimization based on simulated annealing

  • Authors:

  • Kaisa Miettinen;Marko M. Mäkelä;Heikki Maaranen

  • Affiliations:

  • Helsinki School of Economics, P.O. Box 1210, FIN-00101 Helsinki, Finland;Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, (Agora), FIN-40014, Finland;Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, (Agora), FIN-40014, Finland

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2006

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Abstract

We introduce several hybrid methods for global continuous optimization. They combine simulated annealing and a local proximal bundle method. Traditionally, the simplest hybrid of a global and a local solver is to call the local solver after the global one, but this does not necessarily produce good results. Besides, using efficient gradient-based local solvers implies that the hybrid can only be applied to differentiable problems. We show several ways how to integrate the local solver as a genuine part of simulated annealing to enable both efficient and reliable solution processes. When using the proximal bundle method as a local solver, it is possible to solve even nondifferentiable problems. The numerical tests show that the hybridization can improve both the efficiency and the reliability of simulated annealing.