Multi-objective Rao algorithm in resilience-based optimal design of water distribution networks

Abstract

Multi-objective meta-heuristics are used to optimize water distribution networks (WDNs) as they can achieve a near-optimal balance between cost and resilience in a unified platform. Most of these algorithms include tuning of algorithm-specific control parameters for higher optimization efficiency, leading to an increased computational effort. The current study is inspired by the desire to address the above problem. The goal is to formulate a multi-objective Rao algorithm (MORao) considering an existing modified resilience index (MRI) in the optimal design of WDNs. The model is demonstrated to attain Pareto-optimal solutions to complex WDN problems without exclusive parameter tuning. The algorithm is written in Python and is linked to a hydraulic model of a WDN implemented in EPANET 2.2 using pressure-driven demand (PDD) analysis. The method is demonstrated on three widely used networks: Two-loop, Goyang, and Fossolo. The Pareto-optimal solutions examine a tradeoff between two objectives to recognize competitive solutions. The network's resilience is increased 2.5 times by only 0.8 times increase in least-cost of Two-loop network. This research indicates that this method can achieve a satisfactory level of performance with a limited number of function evaluations. HIGHLIGHTS A novel parameter-less Rao algorithm is used in resilience-based optimization of water distribution networks for the first time.; Scalarized objective function is formulated using weights and normalization.; A practical and straightforward approach is proposed and evaluated for the complex design problem.; Approximate Pareto curves are achieved.; Most beneficial alternatives from Pareto optimal sets are selected.;

Keywords

• evolutionary algorithms
• multi-objective optimization
• pressure-driven demand analysis
• rao algorithm
• resilience
• water distribution network