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...

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Bibliographic Details
Published in:2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making pp. 66 - 73
Main Authors: Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2009
Subjects:
Birds
Contracts
Educational institutions
Genetic algorithms
Genetic mutations
Marine animals
Particle swarm optimization
Performance analysis
Polynomials
Proposals
ISBN:1424427649, 9781424427642
Online Access:Get full text
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Summary: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