Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/6017
Title: Adaptive learning particle swarm optimizer-II for global optimization
Authors: Li, C
Yang, S
Keywords: ALPSO;ALPSO-II
Issue Date: 2010
Publisher: IEEE
Citation: IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 779 - 786, 18 - 23 July 2010
Abstract: This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO), we call it ALPSO-II. In order to improve the performance of ALPSO on multi-modal problems, we introduce several new major features in ALPSO-II: (i) Adding particle's status monitoring mechanism, (ii) controlling the number of particles that learn from the global best position, and (iii) updating two of the four learning operators used in ALPSO. To test the performance of ALPSO-II, we choose a set of 27 test problems, including un-rotated, shifted, rotated, rotated shifted, and composition functions in comparison of the ALPSO algorithm as well as several state-of-the-art variant PSO algorithms. The experimental results show that ALPSO-II has a great improvement of the ALPSO algorithm, it also outperforms the other peer algorithms on most test problems in terms of both the convergence speed and solution accuracy.
Description: Copyright @ 2010 IEEE.
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5586230&tag=1
http://bura.brunel.ac.uk/handle/2438/6017
DOI: http://dx.doi.org/10.1109/CEC.2010.5586230
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf306.01 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.