Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
PSO and neural networks: Optimal combination to solve non linear complex problems
Year:2011
Research Areas
  • Engineering
Information
Abstract
Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.
International
Si
JCR
No
Title
Computer Research Today
ISBN
0976-1586
Impact factor JCR
Impact info
Volume
Journal number
From page
1
To page
27
Month
SIN MES
Ranking
Participants
  • Autor: Alberto Arteta Albert (UPM)
  • Autor: L. Mazzel
  • Autor: N. Gómez
  • Autor: L.F. Mingo
Research Group, Departaments and Institutes related
  • Creador: Departamento: Matemática Aplicada (E.U. Informática)
  • Grupo de Investigación: Grupo de Computación Natural
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)