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Efficient hybrid methods for global continuous optimization based on simulated annealing
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DOI：10.1016/j.cor.2004.09.005.png)
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We introduce several hybrid methods for global continuous optimization. They combine simulated annealing and a local proximal bundle method. Traditionally, the simplest hybrid of it 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. (c) 2004 Elsevier Ltd. All rights reserved.
Keyword:
global optimization
metaheuristics
hybridization
bundle methods
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