Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles in Swarm Optimization (PPSO) - which exploits Fuzzy Logic to calculate the best setting for the inertia, cognitive factor and social factor. Thanks to additional heuristics, PPSO automatically determines also the best setting for the swarm size and for the particles maximum velocity. PPSO significantly differs from other versions of PSO that exploit Fuzzy Logic, since specific settings are assigned to each particle according to its history, instead of being globally defined for the whole swarm. Thus, the novelty of PPSO is that particles gain a limited autonomous and proactive intelligence, instead of being simple reactive agents. Our results show that PPSO outperforms the standard PSO, both in terms of convergence speed and average quality of solutions, remarkably without the need for any user setting.

Nobile, M., Pasi, G., Cazzaniga, P., Besozzi, D., Colombo, R., & Mauri, G. (2015). Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc. [10.1109/FUZZ-IEEE.2015.7337957].

Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic

NOBILE, MARCO SALVATORE
Primo
;
PASI, GABRIELLA
Secondo
;
BESOZZI, DANIELA;COLOMBO, RICCARDO
Penultimo
;
MAURI, GIANCARLO
Ultimo
2015

Abstract

Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles in Swarm Optimization (PPSO) - which exploits Fuzzy Logic to calculate the best setting for the inertia, cognitive factor and social factor. Thanks to additional heuristics, PPSO automatically determines also the best setting for the swarm size and for the particles maximum velocity. PPSO significantly differs from other versions of PSO that exploit Fuzzy Logic, since specific settings are assigned to each particle according to its history, instead of being globally defined for the whole swarm. Thus, the novelty of PPSO is that particles gain a limited autonomous and proactive intelligence, instead of being simple reactive agents. Our results show that PPSO outperforms the standard PSO, both in terms of convergence speed and average quality of solutions, remarkably without the need for any user setting.
No
paper
Scientifica
particle swarm optimization; proactive particles in swarm optimization; fuzzy logic; self-tuning algorithms; settings free optimization; global optimization
English
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
9781467374286
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7337957&filter=AND(p_Publication_Number:7329077)
Nobile, M., Pasi, G., Cazzaniga, P., Besozzi, D., Colombo, R., & Mauri, G. (2015). Proactive Particles in Swarm Optimization: A self-tuning algorithm based on Fuzzy Logic. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ : Institute of Electrical and Electronics Engineers Inc. [10.1109/FUZZ-IEEE.2015.7337957].
Nobile, M; Pasi, G; Cazzaniga, P; Besozzi, D; Colombo, R; Mauri, G
File in questo prodotto:
File Dimensione Formato  
ppso.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 1.72 MB
Formato Adobe PDF
1.72 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/96859
Citazioni
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 2
Social impact