0000000001173362
AUTHOR
Christian Kråkevik Haraldseid
A Learning Automata Local Contribution Sampling Applied to Hydropower Production Optimisation
Learning Automata (LA) is a powerful approach for solving complex, non-linear and stochastic optimisation problems. However, existing solutions struggle with high-dimensional problems due to slow convergence, arguably caused by the global nature of feedback. In this paper we introduce a novel Learning Automata (LA) scheme to attack this challenge. The scheme is based on a parallel form of Local Contribution Sampling (LCS), which means that the LA receive individually directed feedback, designed to speed up convergence. Furthermore, our scheme is highly decentralized, allowing parallel execution on GPU architectures. To demonstrate the power of our scheme, the LA LCS is applied to hydropower…
A novel learning automata game with local feedback for parallel optimization of hydropower production
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Hydropower optimization for multi-reservoir systems is classi ed as a combinatorial optimization problem with large state-space that is particularly di cult to solve. There exist no golden standard when solving such problems, and many proposed algorithms are domain speci c. The literature describes several di erent techniques where linear programming approaches are extensively discussed, but tends to succumb to the curse of dimensionality problem when the state vector dimensions increase. This thesis introduces LA LCS, a novel learning automata algorithm that utilizes a parallel form of local feedbac…