Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow

被引:64
|
作者
Roy, P. K. [1 ]
Mandal, D. [2 ]
机构
[1] Dr BC Roy Engn Coll, Dept Elect Engn, Durgapur 713206, W Bengal, India
[2] Birbhum Inst Engn & Technol, Dept Elect Engn, Suri, W Bengal, India
关键词
biogeography-based optimization; optimal power flow; opposition-based learning; evolutionary programming; mutation; migration; ALGORITHM;
D O I
10.1080/15325008.2011.629337
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:236 / 256
页数:21
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