@inproceedings{41fbd27162634fc2b5e1e78d9ac2bedd,
title = "Research on reactive power optimization based on immunity genetic algorithm",
abstract = "This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE 30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than current known algorithms.",
author = "Keyan Liu and Wanxing Sheng and Yunhua Li",
year = "2006",
doi = "10.1007/11816157\_72",
language = "英语",
isbn = "3540372717",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "600--611",
booktitle = "International Conference on Intelligent Computing, ICIC 2006, Proceedings",
address = "德国",
note = "2nd International Conference on Intelligent Computing, ICIC 2006 ; Conference date: 16-08-2006 Through 19-08-2006",
}