Use of Domain Knowledge to Increase the Convergence Rate of Evolutionary Algorithms for Optimizing the Cost and Resilience of Water Distribution Systems

Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details.

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Benchmark Tests of Evolutionary Algorithms: Mathematic Evaluation and Application to Water Distribution Systems

Journal of Environmental Informatics ◽

10.3808/jei.200600064 ◽

2006 ◽

Vol 7 (1) ◽

pp. 24-35 ◽

Cited By ~ 10

Author(s):

B. S. Jung

Keyword(s):

Evolutionary Algorithms ◽

Distribution Systems ◽

Water Distribution ◽

Water Distribution Systems ◽

Benchmark Tests

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Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems

Journal of Hydroinformatics ◽

10.2166/hydro.2009.047 ◽

2009 ◽

Vol 11 (2) ◽

pp. 89-105 ◽

Cited By ~ 16

Author(s):

Ralph J. Olsson ◽

Zoran Kapelan ◽

Dragan A. Savic

Keyword(s):

Genetic Algorithms ◽

Distribution Systems ◽

Water Distribution ◽

Water Distribution Systems ◽

Building Blocks ◽

Probabilistic Methods ◽

Chi Square ◽

Nsga Ii ◽

Multi Objective ◽

The Cost

The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (Mühlenbein 1997), the hierarchical Bayesian Optimisation Algorithm (hBOA) (Pelikan 2002) and the Chi-Square Matrix methodology (Aporntewan & Chongstitvatana 2004)—are compared to the well-known multi-objective evolutionary algorithm NSGAII (Deb et al. 2002) for the multi-objective design and rehabilitation of water distribution systems. For single-objective problems the identification of building blocks has been seen to make evolutionary algorithms more scalable to large problems than simple genetic algorithms. In this paper these algorithms are shown to offer significantly better solutions than NSGA-II for the case of large systems. However, this improvement comes at the expense of diversity of solutions in the fronts identified.

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