A novel learning automata game with local feedback for parallel optimization of hydropower production
Master thesis
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http://hdl.handle.net/11250/2455004Utgivelsesdato
2017Metadata
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Sammendrag
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 feedback. This enables each individual automaton
to receive direct feedback, resulting in faster convergence. In addition, the
algorithm is implemented using a parallel architecture on a CUDA enabled
GPU, along with exhaustive and random search.
LA LCS has been veri ed through several scenarios. Experiments show that
the algorithm is able to quickly adapt and nd optimal production strategies
for problems of variable complexity. The algorithm is empirically veri ed
and shown to hold great promise for solving optimization problems, including
hydropower production strategies.
Beskrivelse
Master's thesis Information- and communication technology IKT590 - University of Agder 2017