Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/18118
Título: | Robustness in multi-objective optimization using evolutionary algorithms |
Autor(es): | Gaspar-Cunha, A. Covas, J. A. |
Palavras-chave: | Multi-objective optimization Evolutionary algorithms Robustness |
Data: | Jan-2008 |
Editora: | Springer |
Revista: | Computational Optimization and Applications |
Resumo(s): | This work discusses robustness assessment during multi-objective optimization with a Multi-Objective Evolutionary Algorithm (MOEA) using a combination of two types of robustness measures. Expectation quantifies simultaneously fitness and robustness, while variance assesses the deviation of the original fitness in the neighborhood of the solution. Possible equations for each type are assessed via application to several benchmark problems and the selection of the most adequate is carried out. Diverse combinations of expectation and variance measures are then linked to a specific MOEA proposed by the authors, their selection being done on the basis of the results produced for various multi-objective benchmark problems. Finally, the combination preferred plus the same MOEA are used successfully to obtain the fittest and most robust Pareto optimal frontiers for a few more complex multi-criteria optimization problems. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/18118 |
DOI: | 10.1007/s10589-007-9053-9 |
ISSN: | 0926-6003 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | IPC - Artigos em revistas científicas internacionais com arbitragem |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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25. Robustness in Multi-Objective Optimization using Evolutionary Algorithms.pdf Acesso restrito! | 2,03 MB | Adobe PDF | Ver/Abrir |