自动化学报2017，Vol.43Issue(1)：83-93,11.DOI:10.16383/j.aas.2017.c150791
迁移蜂群优化算法及其在无功优化中的应用
Transfer Bees Optimizer and Its Application on Reactive Power Optimization
摘要
Abstract
This paper proposes a novel transfer bees optimizer (TBO), which is implemented to solve the reactive power optimization of power systems. The trial-and-error and the reward mechanism of Q-learning is adopted to construct the learning mode of the bees, and the technology of behavior transfer from reinforcement learning is used for transfer learning. Moreover, a space-action chain is proposed to decompose the solution space into several lower-dimensional spaces, thus it can solve the curse of dimension resulted from the multiple variables optimization problem. Simulation results show that TBO can obtain a high-quality optimal solution, while its convergence speed can be accelerated as many as 4 to 67 times faster than that of the conventional heuristic artificial algorithm (AI) algorithm, which is very suitable for fast optimization of nonlinear programming in a large-scale complex system.关键词
迁移蜂群优化/强化学习/行为迁移/无功优化Key words
Transfer bees optimizer (TBO)/reinforcement learning/behavior transfer/reactive power optimization引用本文复制引用
徐茂鑫,张孝顺,余涛..迁移蜂群优化算法及其在无功优化中的应用[J].自动化学报,2017,43(1):83-93,11.基金项目
国家重点基础研究发展计划(973计划)(2013CB228205),国家自然科学基金(51177051,51477055)资助Supported by National Key Basic Research Program of China (973 Program)(2013CB228205) and National Natural Science Foundation of China (51177051,51477055) （973计划）
