  Water distribution system design is a challenging optimisation problem with a
high number of search dimensions and constraints. In this way, Evolutionary
Algorithms (EAs) have been widely applied to optimise WDS to minimise cost
subject whilst meeting pressure constraints. This paper proposes a new hybrid
evolutionary framework that consists of three distinct phases. The first phase
applied CMA-ES, a robust adaptive meta-heuristic for continuous optimisation.
This is followed by an upward-greedy search phase to remove pressure
violations. Finally, a downward greedy search phase is used to reduce oversized
pipes. To assess the effectiveness of the hybrid method, it was applied to five
well-known WDSs case studies. The results reveal that the new framework
outperforms CMA-ES by itself and other previously applied heuristics on most
benchmarks in terms of both optimisation speed and network cost.
