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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| Vydané v: | 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making s. 66 - 73 |
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| Hlavní autori: | , , , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
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IEEE
01.03.2009
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| ISBN: | 1424427649, 9781424427642 |
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| 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 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. |
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| AbstractList | 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. |
| Author | Coello Coello, C.A. Durillo, J.J. Garcia-Nieto, J. Luna, F. Alba, E. Nebro, A.J. |
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| Snippet | 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... |
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| SubjectTerms | Birds Contracts Educational institutions Genetic algorithms Genetic mutations Marine animals Particle swarm optimization Performance analysis Polynomials Proposals |
| Title | SMPSO: A new PSO-based metaheuristic for multi-objective optimization |
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