Generation of complex nonlinear benchmark functions for optimization using fuzzy sets and classical test functions
In this paper, we present a novel methodology to generate complex functionsusing two-dimensional fuzzy sets as weights for combining classical benchmarkfunctions. These new functions have different characteristics from original ones,but the minimum, borders and geometry characteristics of the functi...
- Autores:
-
Mesa, Eddy
Velásquez, Juan David
Jaramillo, Patricia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2011
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- spa
- OAI Identifier:
- oai:repository.udem.edu.co:11407/792
- Acceso en línea:
- http://hdl.handle.net/11407/792
- Palabra clave:
- Minimization
maximization
fuzzy operators
global optimization
test problems.
- Rights
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
| Summary: | In this paper, we present a novel methodology to generate complex functionsusing two-dimensional fuzzy sets as weights for combining classical benchmarkfunctions. These new functions have different characteristics from original ones,but the minimum, borders and geometry characteristics of the functions are stillknown. Three different combinations of two functions (Rosenbrock and Bukin’s F4)are used to exemplify the method and its potential to generate specific test functionsto study and improve optimization methods. |
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