International Journal of Computational Intelligence Systems

Volume 3, Issue 6, December 2010, Pages 832 - 842

Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems

Authors
Lei Gao, Atekelty Hailu
Corresponding Author
Lei Gao
Received 7 May 2010, Accepted 5 October 2010, Available Online 1 December 2010.
DOI
10.2991/ijcis.2010.3.6.13How to use a DOI?
Keywords
Particle swarm optimization, mixed variables, feasibility-based rules, constrained optimization, evolutionary algorithms, comprehensive learning strategy
Abstract

This paper presents an improved particle swarm optimizer (PSO) for solving multimodal optimization problems with problem-specific constraints and mixed variables. The standard PSO is extended by employing a comprehensive learning strategy, different particle updating approaches, and a feasibility-based rule method. The experiment results show the algorithm located the global optima in all tested problems, and even found a better solution than those previously reported in the literature. In some cases, it outperforms other methods in terms of both solution accuracy and computational cost.

Copyright
© 2010, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
3 - 6
Pages
832 - 842
Publication Date
2010/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2010.3.6.13How to use a DOI?
Copyright
© 2010, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Lei Gao
AU  - Atekelty Hailu
PY  - 2010
DA  - 2010/12/01
TI  - Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 832
EP  - 842
VL  - 3
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.6.13
DO  - 10.2991/ijcis.2010.3.6.13
ID  - Gao2010
ER  -