A particle swarm optimizer for multi-objective optimization
This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objectiv...
Guardado en:
| Autores principales: | , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2005
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9594 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Dec05-7.pdf |
| Aporte de: |
| Sumario: | This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use three well-known test functions proposed in the specialized literature. Preliminary simulations results are presented and compared with those obtained with the Pareto Archived Evolution Strategy (PAES) and the Multi-Objective Genetic Algorithm 2 (MOGA2). These results also show that the SMOPSO algorithm is a promising alternative to tackle multiobjective optimization problems. |
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