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Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow.
- Source :
-
Electric Power Components & Systems . Jan2012, Vol. 40 Issue 2, p236-256. 21p. - Publication Year :
- 2012
-
Abstract
- This article develops an efficient and reliable evolutionary programming algorithm, namely quasi-oppositional biogeography-based optimization, for solving optimal power flow problems. To improve the simulation results as well as the speed of convergence, opposition-based learning is incorporated in the original biogeography-based optimization algorithm. In order to investigate the performance, the proposed scheme is applied on optimal power flow problems of standard 26-bus, IEEE 118-bus, and IEEE 300-bus systems; and comparisons among mixed-integer particle swarm optimization, evolutionary programming, the genetic algorithm, original biogeography-based optimization, and quasi-oppositional biogeography-based optimization are presented. The results show that the new quasi-oppositional biogeography-based optimization algorithm outperforms the other techniques in terms of convergence speed and global search ability. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 15325008
- Volume :
- 40
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Electric Power Components & Systems
- Publication Type :
- Academic Journal
- Accession number :
- 69603790
- Full Text :
- https://doi.org/10.1080/15325008.2011.629337