An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources

Zheng, Feifei; Zecchin, Aaron C.

doi:10.1016/j.envsoft.2014.01.028

Cited by 51 publications

(22 citation statements)

References 35 publications

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“…tracking the iteration-best objective function value (e.g. , or the evolution of the Pareto-front of non-dominated solutions (Zheng and Zecchin, 2014). Measures in the objective space have typically been used to compare an algorithm's end-of-run performance (see Section 2.4 for a broad overview of these performance metrics), whereas behavioural analysis focuses on the run-time tracking of these measures.…”

Section: Current Statusmentioning

confidence: 99%

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

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Current Statusmentioning

confidence: 99%

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

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

View full text Add to dashboard Cite

show abstract

“…NSGA-II [99] is an improved version of NSGA and has been popularly applied to WDN optimization [106], [107] with low computational complexity and ability to find a good set of diverse solutions. The minimum nodal head across the whole network and the capital cost were considered as the two objectives in [108] and the optimization was undertaken by the proposal of a computationally efficient decomposition and dual-stage multi-objective optimization (DDMO) method. In DDMO, the original network was first broken down into a number of small sub-networks by using a graph decomposition algorithm.…”

Section: E Multi-objective Optimization Approachesmentioning

confidence: 99%

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Zhao

Beach

Rezgui

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. Firstly, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. The paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners.

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“…In multi-objective problems, a Pareto-optimal, or non-dominated, solution outperforms all other solutions with respect to all objectives . Multi-objective evolutionary algorithms (MOEAs) have been shown to be effective in providing Pareto-optimal solutions for a large number of subsurface flow applications possessing several decision variables (Alzraiee et al 2013, Baú 2012, Chen et al 2007, Hansen et al 2013, Kumphon 2013, Mantoglou and Kourakos 2006, Nicklow et al 2010, Peralta et al 2014, Singh 2013, Singh 2014, Singh and Chakrabarty 2010, Tabari and Soltani 2012, Zheng, F., Zecchin 2014. In particular, ) presents a comprehensive review of state-of-the-art MOEAs highlighting key algorithm advances which may be used to identify critical tradeoffs in water resources problems.…”

Section: Introductionmentioning

confidence: 99%

Optimisation stochastique de la stratégie d’injection en vue de la pré-évaluation de sites candidats au stockage géologique

Cody¹,

Baù

González‐Nicolás

2015

Hydrogeol J

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Geological carbon sequestration (GCS) has been identified as having the potential to reduce increasing atmospheric concentrations of carbon dioxide (CO 2 ). However, a global impact will only be achieved if GCS is cost-effectively and safely implemented on a massive scale. This work presents a computationally efficient methodology for identifying optimal injection strategies at candidate GCS sites having uncertainty associated with caprock permeability, effective compressibility, and aquifer permeability. A multi-objective evolutionary optimization algorithm is used to heuristically determine non-dominated solutions between the following two competing objectives: 1) maximize mass of CO 2 sequestered and 2) minimize project cost. A semi-analytical algorithm is used to estimate CO 2 leakage mass rather than a numerical model, enabling the study of GCS sites having vastly different domain characteristics. The stochastic optimization framework presented herein is applied to a feasibility study of GCS in a brine aquifer in the Michigan Basin (MB), USA. Eight optimization test cases are performed to investigate the impact of decision-maker (DM) preferences on Pareto-optimal objective-function values and carbon-injection strategies. This analysis shows that the feasibility of GCS at the MB test site is highly dependent upon the DM's risk-adversity preference and degree of uncertainty associated with caprock integrity. Finally, large gains in computational efficiency achieved using parallel processing and archiving are discussed.

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An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources

Cited by 51 publications

References 35 publications

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

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

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Optimisation stochastique de la stratégie d’injection en vue de la pré-évaluation de sites candidats au stockage géologique

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