This paper describes a novel initialization for Deterministic Particle Swarm Optimization (DPSO), based on choosing specific dense initial positions and velocities for particles. This choice tends to induce orthogonality of particles' trajectories, in the early iterations, in order to better explore the search space. Our proposal represents an improvement, by the same authors, of the theoretical analysis on a previously proposed PSO reformulation, namely the initialization ORTHOinit. A preliminary experience on constrained Portfolio Selection problems confirms our expectations.

Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit

FASANO, Giovanni;GUSSO, Riccardo
2016-01-01

Abstract

This paper describes a novel initialization for Deterministic Particle Swarm Optimization (DPSO), based on choosing specific dense initial positions and velocities for particles. This choice tends to induce orthogonality of particles' trajectories, in the early iterations, in order to better explore the search space. Our proposal represents an improvement, by the same authors, of the theoretical analysis on a previously proposed PSO reformulation, namely the initialization ORTHOinit. A preliminary experience on constrained Portfolio Selection problems confirms our expectations.
2016
ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I
File in questo prodotto:
File Dimensione Formato  
llncs_INSEAN_R1_finale.pdf

accesso aperto

Descrizione: paper
Tipologia: Documento in Pre-print
Licenza: Accesso libero (no vincoli)
Dimensione 305.42 kB
Formato Adobe PDF
305.42 kB Adobe PDF Visualizza/Apri

I documenti in ARCA 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: https://hdl.handle.net/10278/3684793
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact