A reinforcement learning hyper-heuristic for water distribution network optimisation

The Water Distribution Networks (WDNs) optimisation problem focuses on finding the combination of pipes from a collection of discrete sizes available to construct a network of pipes with minimum monetary cost. It is one of the most significant problems faced by WDN engineers. This problem belongs to the class of difficult combinatorial optimisation problems, whose optimal solution is hard to find, due to its large search space. Hyper-heuristics are high-level search algorithms that explore the space of heuristics rather than the space of solutions in a given optimisation problem. In this work, different selection hyper-heuristics were proposed and empirically analysed in the WDN optimisation problem, with the goal of minimising the network’s cost. New York Tunnels network benchmark was used to test the performance of these hyper-heuristics including the Reinforcement Learning (RL) hyper-heuristic method, that succeeded in achieving improved results