A new algorithm for identifying all global maximizers based on simulated annealing
In this work we consider the problem of finding all the global maximizers of a given nonlinear optimization problem. We propose a new algorithm that combines the simulated annealing (SA) method with a function stretching technique, to generate a sequence of global maximization problems that are defi...
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| Formato: | conferencePaper |
| Idioma: | eng |
| Publicado em: |
2005
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| Texto completo: | http://hdl.handle.net/1822/5417 |
| País: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/5417 |
| Resumo: | In this work we consider the problem of finding all the global maximizers of a given nonlinear optimization problem. We propose a new algorithm that combines the simulated annealing (SA) method with a function stretching technique, to generate a sequence of global maximization problems that are defined whenever a new maximizer is identified. To find the global maximizers, we apply the SA algorithm to the sequence of maximization problems. Results of numerical experiments with a set of well-known test problems show that the proposed method is effective. We also compare the performance of our algorithm with other multi-global optimizers. |
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