Presentation
SMPSO: A new PSO-based metaheuristic for multi-objective optimization
Author/s | Nebro, Antonio J.
Durillo, Juan J. García Nieto, José Manuel Coello Coello, Carlos A. Luna, Francisco Alba, Enrique |
Department | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Publication Date | 2009 |
Deposit Date | 2021-05-13 |
Published in |
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ISBN/ISSN | 978-1-4244-2764-2 |
Abstract | In this work, we present a new multi-objective
particle swarm optimization algorithm (PSO) characterized by
the use of a strategy to limit the velocity of the particles.
The proposed approach, called Speed-constrained ... In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrainedMulti-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the nondominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed. |
Funding agencies | Junta de Andalucía |
Project ID. | P07-TIC-03044
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Citation | Nebro, A.J., Durillo, J.J., García Nieto, J.M., Coello Coello, C.A., Luna, F. y Alba, E. (2009). SMPSO: A new PSO-based metaheuristic for multi-objective optimization. En MCDM 2009: IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (66-73), Nashville, TN, USA: IEEE Computer Society. |
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