摘要
针对遗传算法存在的早熟和收敛慢的问题,提出一种融合小生境算法、免疫算法的优化遗传算法。一方面通过疫苗因子引导初始种群的生成,使个体具有某些优秀基因,减少寻优时间,并随数据的更新,提出疫苗因子和参数寻优范围的自适应更新机制。另一方面在种群的进化过程中,通过小生境遗传算法维护种群的多样性。实验结果表明,将基于优化遗传算法寻优的SVM应用到短期风速预测中是可行的,具有较高的预测精度和收敛速度。
An improved genetic algorithm combined with niche algorithm and immune algorithm is proposed to solve the standard genetic algorithm for prematurity and slow convergence rate. On one hand, vaccine is introduced to guide the generation of initial population, so the individual has some excellent gens which will reduce the optimal time, and an adaptive updating mechanism of vaccine and parameter scope is proposed as the wind speed data renewed. On the other hand, niche genetic algorithm is adopted to keep the diversity of evolution population. Examples show that it is feasible to apply the proposed method in short-term wind speed prediction, with higher forecasting accuracy and convergence speed.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2016年第9期38-42,共5页
Power System Protection and Control
基金
广西高校科学技术研究项目(KY2015YB312)
广西壮族自治区中青年教师基础能力提升项目资助(基于IA-NGA算法寻优的SVM短期风能预测研究)
关键词
优化遗传算法
短期风速预测
SVM
参数寻优
自适应更新
improved genetic algorithm
short-term wind speed prediction
SVM
parameter selection
adaptive updating
