A distributed agent-based approach for simulation-based optimization

Nguyễn, Văn Vịnh; Hartmann, Dietrich; König, Markus

doi:10.1016/j.aei.2012.06.001

Cited by 11 publications

(9 citation statements)

References 48 publications

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“…Both strategies are implemented in the aforementioned optimization framework MOPACK being part of the integrated monitoring system. MOPACK, a Java-based optimization framework that integrates numerous optimization strategies such as Evolution Strategies and Differential Evolution (Nguyen et al 2010), is interfaced with the finite element software used (ANSYS in this study). According to the optimization strategy chosen, the finite element analyses using ANSYS are carried out repeatedly in a MOPACK-driven optimization loop to compute the required quantities, i.e.…”

Section: Model Updating and Damage Detectionmentioning

confidence: 99%

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An integrated monitoring system for life-cycle management of wind turbines

Smarsly¹,

Hartmann²,

Law³

2013

Smart Structures and Systems

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Abstract.With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for selfdiagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

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Section: Model Updating and Damage Detectionmentioning

confidence: 99%

“…The model parameters are varied until the wind turbine model approximates the behavior of the physical wind turbine structure. For that purpose, the optimization framework "Multi-method Optimization PACKage (MOPACK)", developed at ICE, is used (Nguyen et al 2010).…”

Section: Model Updatingmentioning

confidence: 99%

An integrated monitoring system for life-cycle management of wind turbines

Smarsly¹,

Hartmann²,

Law³

2013

Smart Structures and Systems

Self Cite

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“…For the domain of tunneling, a demonstration of a methodology of how discrete event simulations and fuzzy expert systems can be integrated is given in Shaheen et al (2009). Further, Kulejewski (2011) investigates how fuzzy numbers and alpha-cuts of a fuzzy number can be used to model imprecision of constraints. In this case alpha-cuts are used to assess a certain probability about the uncertainty regarding constraints.…”

Section: Related Workmentioning

confidence: 99%

Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory

Szczesny¹,

KÃ¶nig²,

LauÃat³

et al. 2013

Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2013): Building Th

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This paper considers the integration of uncertain real-time logistics data for reactive construction scheduling. In order to manage a construction project efficiently, an accurate schedule representing the current project progress is inevitable. The quality and up-to-dateness of such a schedule depends on the availability of real-time data. Typically, real-time logistics data contain information about the availability of material, equipment and personnel as well as delivery dates and site conditions. The accuracy and inherent uncertainty depends on the location where the real-time data was acquired. Currently, the integration of such data into a construction schedule is a very time-consuming, manual and, thus, errorprone process. Therefore, this paper proposes a methodology that enables an automatic integration of such uncertain data into construction schedules. By integrating uncertainties into the existing schedule their impacts on the construction work can be evaluated. For this, discrete event simulation is applied. In order to model uncertain input parameters for simulation models this methodology applies the fuzzy sets theory. In combination with alpha-cut sampling technique, discrete model input parameters are obtained. By applying reactive scheduling with several discrete event simulation experiments, the results can be used to modify construction schedules according to agreed timeframes and costs. In order to demonstrate and validate the presented approach an example is conducted.

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“…Problems like the one described in the previous section may be categorized among the so-called non-standard optimization problems [37], which are being increasingly used in engineering optimization and, in general, in real-world optimization problems. One of the main characteristics of this category of problems is that relevant optimization criteria, as well as prescribed constraints, can only be evaluated by virtue of computer simulations (simulationbased optimization).…”

Section: Some Weaknesses Of Evolutionary Approachesmentioning

confidence: 99%

Injecting problem-dependent knowledge to improve evolutionary optimization search ability

Izquierdo

Campbell

Montalvo³

et al. 2016

Journal of Computational and Applied Mathematics

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ElsevierIzquierdo Sebastián, J.; Campbell-Gonzalez, E.; Montalvo Arango, I.; Pérez García, R. (2016) ABSTRACTThe flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints -even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystalizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence.

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A distributed agent-based approach for simulation-based optimization

Cited by 11 publications

References 48 publications

An integrated monitoring system for life-cycle management of wind turbines

An integrated monitoring system for life-cycle management of wind turbines

Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory

Injecting problem-dependent knowledge to improve evolutionary optimization search ability

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