Sensitivity of algorithm parameters and objective function scaling in multi-objective optimisation of water distribution systems

This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational object...

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Bibliographic Details
Published in:Journal of hydroinformatics Vol. 17; no. 6; pp. 891 - 916
Main Authors: Mala-Jetmarova, Helena, Barton, Andrew, Bagirov, Adil
Format: Journal Article
Language:English
Published: London IWA Publishing 01.11.2015
Subjects:
Algorithms
Analysis
Computer programs
Costs
Distribution
Drinking water
Emission standards
Emissions
Energy costs
Energy measurement
Fronts
Genetic algorithms
Greenhouse gases
Hydraulics
Mathematical models
Multiple objective analysis
Mutation
Network analysis
Objective function
Objectives
Optimization
Parameter sensitivity
Parameters
Pareto optimum
Performance measurement
Rehabilitation
Scaling
Sensitivity analysis
Simulation
Software
Solutions
Water distribution
Water distribution systems
Water engineering
Water quality
Water quality measurements
Water shortages
Water supply
Australia
Switzerland
ISSN:1464-7141, 1465-1734
Online Access:Get full text
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Summary:This paper presents an extensive analysis of the sensitivity of multi-objective algorithm parameters and objective function scaling tested on a large number of parameter setting combinations for a water distribution system optimisation problem. The optimisation model comprises two operational objectives minimised concurrently, the pump energy costs and deviations of constituent concentrations as a water quality measure. This optimisation model is applied to a regional non-drinking water distribution system, and solved using the optimisation software GANetXL incorporating the NSGA-II linked with the network analysis software EPANet. The sensitivity analysis employs a set of performance metrics, which were designed to capture the overall quality of the computed Pareto fronts. The performance and sensitivity of NSGA-II parameters using those metrics is evaluated. The results demonstrate that NSGA-II is sensitive to different parameter settings, and unlike in the single-objective problems, a range of parameter setting combinations appears to be required to reach a Pareto front of optimal solutions. Additionally, inadequately scaled objective functions cause the NSGA-II bias towards the second objective. Lastly, the methodology for performance and sensitivity analysis may be used for calibration of algorithm parameters.
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ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2015.062