Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization
Accelerating bio-inspired optimizer with transfer reinforcement learning for reactive power optimization
复制标题
通过转移强化学习加速仿生优化器以实现无功功率优化
DOI：
10.1016/j.knosys.2016.10.024
复制
发表时间：
2017-01
影响因子：
8.8
通讯作者：
Yu T
中科院分区：
文献类型：
--
作者：
Zhang Xiaoshun;Yu Tao;Cheng Lefeng;Yang Bo;Yu T
本文提出了一种与转移强化学习（TRL）相关的新型加速仿生优化器（ABO）来解决大规模电力系统中的无功功率优化（RPO）问题。采用记忆矩阵来表示不同状态-动作对的记忆，用于不同优化任务之间的知识学习、存储和传递。然后引入一种关联存储器来显着降低存储器矩阵的维数，其中多个元素可以由协作的多生物体同时更新。获胜或学习快速爬山策略（WoLF-PHC）也用于加速收敛。因此，ABO 可以根据源任务的相似性利用源任务的先验知识，快速寻求最接近精确全局最优的解。分别在 IEEE 118 总线系统和 IEEE 300 总线系统上针对 RPO 评估了 ABO 的性能。仿真结果验证了ABO在全局收敛能力和稳定性方面优于现有的人工智能算法，可以比其他算法提高一个数量级的收敛速度。
This paper proposes a novel accelerating bio-inspired optimizer (ABO) associated with transfer reinforcement learning (TRL) to solve the reactive power optimization (RPO) in large-scale power systems. A memory matrix is employed to represent the memory of different state-action pairs, which is used for knowledge learning, storage, and transfer among different optimization tasks. Then an associative memory is introduced to significantly reduce the dimension of memory matrix, in which more than one element can be simultaneously updated by the cooperating multi-bion. The win or learn fast policy hill-climbing (WoLF-PHC) is also used to accelerate the convergence. Thus, ABO can rapidly seek the closest solution to the exact global optimum by exploiting the prior knowledge of the source tasks according to their similarities. The performance of ABO has been evaluated for RPO on IEEE 118-bus system and IEEE 300-bus system, respectively. Simulation results verify that ABO outperforms the existing artificial intelligence algorithms in terms of global convergence ability and stability, which can raise one order of magnitude of the convergence rate than that of others.
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DOI：
10.1109/icsmc.2009.5346043
发表时间：
2009-10
期刊：
2009 IEEE International Conference on Systems, Man and Cybernetics
影响因子：
--
作者：
Mehmet Ergezer;D. Simon;Dawei Du
通讯作者：
Mehmet Ergezer;D. Simon;Dawei Du
影响因子：
6.6
作者：
Ramtin Madani;Morteza Ashraphijuo;J. Lavaei
通讯作者：
Ramtin Madani;Morteza Ashraphijuo;J. Lavaei
影响因子：
6.6
作者：
B. Kazemtabrizi;E. Acha
通讯作者：
B. Kazemtabrizi;E. Acha
DOI：
--
发表时间：
2018-09
期刊：
--
影响因子：
--
作者：
S. Adhirai;R. Mahapatra;Paramjit Singh
通讯作者：
S. Adhirai;R. Mahapatra;Paramjit Singh
影响因子：
9
作者：
Chen, Zeyu;Xiong, Rui;Cao, Jiayi
通讯作者：
Cao, Jiayi