A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems

Mirghani, Baha; Mahinthakumar, K.; Tryby, Michael E.; Ranjithan, Ranji; Zechman, Emily M.

doi:10.1016/j.advwatres.2009.06.001

Cited by 85 publications

(34 citation statements)

References 22 publications

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“…Garret et al [9], used parallel real coded GAs for bioremediation of groundwater contamination. More recently in 2009, Mirghani et al [14] developed a parallel evolutionary strategy based simulation optimization approach for solving groundwater source identification problems.…”

Section: Applicationsmentioning

confidence: 99%

Applications of parallel genetic algorithms in ground water contamination problems: An overview

Sharma

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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Groundwater resource management is a challenging problem faced by almost all the countries. Mathematical models of these problems often turn out to be illdefined subject to several variables and constraints. Sophisticated algorithms are needed in order to deal efficiently with such problems. In the past few decades much attention has been paid to heuristic techniques like genetic algorithms etc which can easily solve such problems. Further, in order to tackle the large number of involved parameters in these problems parallel version of GAs is more effective than the basic GAs. In this paper an attempt is made to review the application of PGA on groundwater management problems.

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Section: Applicationsmentioning

confidence: 99%

Applications of parallel genetic algorithms in ground water contamination problems: An overview

Sharma

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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“…To solve the aforementioned problem, an optimization framework is introduced that uses simulations to obtain the result (see Figure 3) [23]. An optimization model is coupled with the simulation model by providing various input trial variables and retrieving simulated results.…”

Section: B Simulation and Optimization Frameworkmentioning

confidence: 99%

A Simulation Study on Urban Water Threat Detection in Modern Cyberinfrastructures

Wang

Chen²,

Deng³

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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The computation of Contaminant Source Characterization (CSC) is a critical research issue in Water Distribution System (WDS) management. We use a simulation framework to identify optimized locations of sensors that lead to fast detection of contamination sources. The optimization engine is based on a Genetic Algorithm (GA) that interprets trial solutions as individuals. During the optimization process many thousands of these solutions are generated. For a large WDS, the calculation of these solutions are non-trivial and time consuming. Hence, it is a compute intensive application that requires significant compute resources. Furthermore, we strive to generate solutions quickly in order to respond to the urgency of a response.To carry out the calculations we require user-level middleware that can be supporting the workflow of the application and manages the resource assignment in an efficient and fault tolerant fashion. To do so we have prototyped the middleware framework that provides a convenient command line and portal layer of steering applications on Grids. Internally, we utilize a sophisticated workflow engine that provides the ability to access elementary fault tolerant mechanisms for job scheduling. This includes the management of job replicas and the reaction on late return of results.We report the test results of CSC problem solving on a real Grid test bed -the TeraGrid test bed. In addition, we contrast this system architecture with a Hadoop-based implementation that automatically includes fault tolerance. The later activity has been conducted on FutureGrid.

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“…Tools such as simulation-optimisation (Barlow et al, 2003;Young, 1983, 1970;Morel Seytoux, 1975;Raul and Panda, 2013;Sedki and Ouazar, 2011;Young and Bredehoeft, 1972), evolutionary algorithms (BabbarSebens andMinsker, 2010, 2012;McKinney and Lin, 1994;Mirghani et al, 2009), econometric models (Brozovic et al, 2010;Katic and Grafton, 2012;Wan et al, 2012), game theory (Negri, 1989;Raquel et al, 2007;Saak and Peterson, 2007), and Bayesian networks (Henriksen and Barlebo, 2008;Henriksen et al, 2007;Portoghese et al, 2013) focus on equilibrium states (e.g., a global optimum, a Nash equilibrium), and describe social processes in an aggregate manner (e.g., using an optimisation function, a differential equation, a payoff matrix, etc.) based on the concept of a 'typical' agent assumed to be on average rational i.e.…”

Section: Introductionmentioning

confidence: 99%

An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems

Castilla-Rho

Mariethoz

Rojas

et al. 2015

Environmental Modelling & Software

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A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems

Cited by 85 publications

References 22 publications

Applications of parallel genetic algorithms in ground water contamination problems: An overview

Applications of parallel genetic algorithms in ground water contamination problems: An overview

A Simulation Study on Urban Water Threat Detection in Modern Cyberinfrastructures

An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems

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