Computational methods for solving multi-objective uncertain optimization problems
In recent years, there has been an increasing interest in the multi-objective uncertain optimization, discussed in the framework of the interval-valued optimization, as a consequence theoretical developments have achieved significant results as theorems analogous to the conditions of Karush Kunt Tuc...
- Autores:
-
Puerta Yepes, María Eugenia
Cano Cadavid, Andrés Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2011
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/4557
- Acceso en línea:
- http://hdl.handle.net/10784/4557
- Palabra clave:
- Rights
- License
- Acceso restringido
| Summary: | In recent years, there has been an increasing interest in the multi-objective uncertain optimization, discussed in the framework of the interval-valued optimization, as a consequence theoretical developments have achieved significant results as theorems analogous to the conditions of Karush Kunt Tucker, but computational developments are still incipient. This paper makes an extension of Strength Pareto Evolutionary Algorithm 2 - SPEA2 - and Multi-objective Particle Swarm Optimization - MOPSO -, which ones are traditionally used in multi-objective optimization, these are modified to the case of multi-objective uncertain optimization, where the model uses the interval-valued optimization as shown by Wu [?, ?, ?], these new algorithms have arithmetic advantage in the image set of the objective function. At the end, numerical examples are shown where they applied the algorithms implemented. |
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