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

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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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  -