Publicação
Evaluation of simulated annealing to solve fuzzy optimization problems
| Resumo: | Simulated Annealing (SA) is a reasonable algorithm for solving optimization problems, through the selection of the best solution among a finite number of possible solutions. It is a particularly attractive technique to solve fuzzy optimization problems, because it allows finding near-optimal solutions, which, in a fuzzy environment, is usually good enough and without a large computational effort. We present a representative set of problems for testing the SA algorithm suitability and performance. The SA performance is measured in terms of the objective function values, considering several trade-offs on constraints satisfaction levels, and computational time to achieve a solution. Furthermore, we discuss the parameters that control the SA algorithm to show how easily they can be manipulated. The set of fuzzy optimization problems tested were formulated following the complete fuzzification method proposed by Ribeiro and Moura-Pires (1999). The results obtained show the flexibility and adaptability of the SA algorithm to solve fuzzy optimization problems. |
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| Autores principais: | Varela, M.L.R. |
| Outros Autores: | Ribeiro, Rita Almeida |
| Assunto: | fuzzy optimization simulated annealing fuzzy constraints fuzzy coefficients |
| Ano: | 2003 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Simulated Annealing (SA) is a reasonable algorithm for solving optimization problems, through the selection of the best solution among a finite number of possible solutions. It is a particularly attractive technique to solve fuzzy optimization problems, because it allows finding near-optimal solutions, which, in a fuzzy environment, is usually good enough and without a large computational effort. We present a representative set of problems for testing the SA algorithm suitability and performance. The SA performance is measured in terms of the objective function values, considering several trade-offs on constraints satisfaction levels, and computational time to achieve a solution. Furthermore, we discuss the parameters that control the SA algorithm to show how easily they can be manipulated. The set of fuzzy optimization problems tested were formulated following the complete fuzzification method proposed by Ribeiro and Moura-Pires (1999). The results obtained show the flexibility and adaptability of the SA algorithm to solve fuzzy optimization problems. |
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