Meta-Modeling in Multiobjective Optimization

Knowles, Joshua; Nakayama, Hirotaka

doi:10.1007/978-3-540-88908-3_10

Cited by 75 publications

(75 citation statements)

References 33 publications

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“…Any remaining difference between the measured data and the best solution is due to missing parameters in the simulation, e.g., the finite conductivity of the conductive parts is not taken into account. Future work includes full inverse electrical characterization of textile materials by including the finite conductivity and extensions to Multiobjective Surrogate-Based Optimization (MOSBO) [44] methods. …”

Section: Discussionmentioning

confidence: 99%

Surrogate-based infill optimization applied to electromagnetic problems

Couckuyt

Declercq

Dhaene

et al. 2010

Int J RF and Microwave Comp Aid Eng

126

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The increasing use of expensive computer simulations in engineering places a serious computational burden on associated optimization problems. Surrogate-based optimization becomes standard practice in analyzing such expensive black-box problems. This article discusses several approaches that use surrogate models for optimization and highlights one sequential design approach in particular, namely, expected improvement. The expected improvement approach is demonstrated on two electromagnetic problems, namely, a microwave filter and a textile antenna.

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Section: Discussionmentioning

confidence: 99%

Surrogate-based infill optimization applied to electromagnetic problems

Couckuyt

Declercq

Dhaene

et al. 2010

Int J RF and Microwave Comp Aid Eng

126

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“…There are a number of reviews of the use of these techniques in optimisation, including Jin (2005) and Knowles and Nakayama (2008), as well as a dedicated edited volume on "Computational Intelligence in Expensive Optimization Problems" (Tenne and Goh, 2010) (in particular, reviews by Shi and Rasheed (2010) and Santana-Quintero et al (2010) therein). In addition, Razavi et al (2012) presented an extensive review of surrogate modelling in water resources and recent developments and applications to environmental systems are also presented in a special issue on "Emulation techniques for the reduction and sensitivity analysis of complex environmental models" (see Ratto et al, 2012).…”

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

524

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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“…Kriging surrogate models are also known as Gaussian Processes (GP) [13] or Gaussian Random Fields [14]. Originally proposed by Krige [15], Kriging was popularized for the Design and Analysis of Computer Experiments (DACE) by Sacks et al [16], where it has proven to be very useful for tasks such as optimization [17,18], design space exploration, visualization, prototyping, and sensitivity analysis [19,20]. For a full survey of Kriging the reader is referred to [12] and [13].…”

Section: Kriging Interpolationmentioning

confidence: 99%

Building accurate radio environment maps from multi-fidelity spectrum sensing data

et al. 2015

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In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g. Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated.

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Meta-Modeling in Multiobjective Optimization

Cited by 75 publications

References 33 publications

Surrogate-based infill optimization applied to electromagnetic problems

Surrogate-based infill optimization applied to electromagnetic problems

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

Building accurate radio environment maps from multi-fidelity spectrum sensing data

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