Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics

Abstract

This study investigates the use of many-objective optimization for water distribution system (WDS) design or rehabilitation problems. The term many-objective optimization refers to optimization with four or more objectives. The increase in the number of objectives brings new challenges for both optimization and visualization. This study uses a multi-objective evolutionary algorithm termed the epsilon Nondominated Sorted Genetic Algorithm II (ε-NSGAII) and interactive visual analytics to reveal and explore the tradeoffs for the Anytown network problem. The many-objective formulation focuses on a suite of six objectives: (1) capital cost, (2) operating cost, (3) hydraulic failure, (4) leakage, (5) water age and (6) fire fighting capacity. These six objectives are optimized based on decisions related to pipe sizing, tank siting, tank sizing, and pump scheduling under five different loading conditions. Solving the many-objective formulation reveals complex tradeoffs that would not be revealed in a lower-dimensional optimization problem. Visual analytics are used to explore these complex tradeoffs and identify solutions that simultaneously improve the overall WDS performance but with reduced capital and operating costs. This study demonstrates that the many-objective visual analytics approach has clear advantages and benefits in supporting more informed, transparent decision making in the WDS design process.

Lost in Optimisation of Water Distribution Systems? A Literature Review of System Design

“…Thirdly, a pump schedule, which describes when the pump is on and off during a scheduling period (e.g., 24 h). It can be specified by a pumping power [53,54] or pump head [123] at each time step, the number of pumps in operation during 24 h [97], binary pump statuses [29], continuous options representing on/off times with a limit imposed on the number of pump switches [76], discrete options representing the time at which a pump is turned on/off using a predefined time step (e.g., 30 min) [75]. All of these decisions impact on the size of the search space and eventually on the computational efficiency of the optimisation algorithm used.…”

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

“…Although this approach is also not without its drawbacks, i.e. when deliberating on final trade-offs, as discussed by Beh et al (2015b); however methods exist to aid the final decision process, such as value path plots (Geoffrion et al 1972) and visual analytics (Reed and Kollat 2013 (Borgwardt 1987) or combined process approaches such as Many-Objective Visual Analytics (Fu et al 2013), Many-Objective Robust Decision Making (MORDM) or Borg MultiObjective Evolutionary Algorithms (MOEA) (Hadka and Reed 2012).…”

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

“…One method used to visually assess algorithm performance is by a set of comparisons, each showing two of the objective function values of the solutions (e.g. Fu et al, 2012). Although this does not give a measure of how much an algorithm is better or worse than another, it highlights whether an algorithm is performing well in a particular region of the search space.…”

“…For a relatively small number of objectives, colour coding and sizes of symbols can be used in order to assess solutions in higher dimensions (e.g. Fu et al, 2012;Kasprzyk et al, 2013).…”