SMPSO: A new PSO-based metaheuristic for multi-objective optimization

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 Multi-objective PSO (SMPSO) allows to produce new effective particle positions in...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making S. 66 - 73
Hauptverfasser: Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2009
Schlagworte:
Birds
Contracts
Educational institutions
Genetic algorithms
Genetic mutations
Marine animals
Particle swarm optimization
Performance analysis
Polynomials
Proposals
ISBN:1424427649, 9781424427642
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 Multi-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 non-dominated 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.
ISBN:1424427649
9781424427642
DOI:10.1109/MCDM.2009.4938830