An approximation algorithm for fuzzy polynomial interpolation with Artificial Bee Colony algorithm
[Display omitted] ► We approximate fuzzy interpolation by using Artificial Bee Colony Algorithm (ABC) for reach to fuzzy global solution of fuzzy optimization problems. ► We modified ABC (MABC) algorithm and use to perform the required task. ► We compare our results with other methods including leas...
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| Veröffentlicht in: | Applied soft computing Jg. 13; H. 4; S. 1997 - 2002 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.04.2013
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| Schlagworte: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | [Display omitted]
► We approximate fuzzy interpolation by using Artificial Bee Colony Algorithm (ABC) for reach to fuzzy global solution of fuzzy optimization problems. ► We modified ABC (MABC) algorithm and use to perform the required task. ► We compare our results with other methods including least square and other evolutionary algorithms like GA and PSO. ► We show that MABC outperforms all of them most of the times.
In this paper, a novel approximation algorithm for fuzzy polynomial interpolation using Artificial Bee Colony algorithm to interpolate fuzzy data is discussed. However, we use our modified ABC (MABC; Mansouri et al. [13]) to perform the required task. Some examples (including the benchmark functions Griewank and Rastrigin) illustrate the rationality of the method and the validity of the solution. We compare our results with other methods including Genetic Algorithm (GA), Particle Swarm Algorithm (PSO). The results show that proposed method outperforms the other algorithms. |
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| ISSN: | 1568-4946 1872-9681 |
| DOI: | 10.1016/j.asoc.2012.11.040 |