Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization
This work addresses the problem of efficient and cost-effective water distribution system design for engineers and planners, representing an incremental improvement over prior evolutionary algorithms.
The paper tackled the optimization of water distribution system design by proposing a new hybrid evolutionary framework that combines CMA-ES with greedy search phases, resulting in improved optimization speed and reduced network cost compared to existing methods on most benchmarks.
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.