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Global optimization of expensive black-box models based on asynchronous hybrid-criterion with interval reduction [article]

Chunlin Gong, Xu Li, Hua Su, Jinlei Guo, Liangxian Gu
<span title="2018-11-29">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, a new sequential surrogate-based optimization (SSBO) algorithm is developed, which aims to improve the global search ability and local search efficiency for the global optimization of expensive  ...  First, to capture the promising possible global optimal region, searching for the global optimum with genetic algorithm (GA) based on the current surrogate models of the objective and constraint functions  ...  The procedure of the sequential surrogate-based optimization method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.12142v1">arXiv:1811.12142v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xhsaazqe7zhudioekz3j3pfy2e">fatcat:xhsaazqe7zhudioekz3j3pfy2e</a> </span>
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Sequential ensemble optimization based on general surrogate model prediction variance and its application on engine acceleration schedule design

Yifan Ye, Zhanxue Wang, Xiaobo Zhang
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7j7rymwfdvdgha64ol5ajggjqa" style="color: black;">Chinese Journal of Aeronautics</a> </i> &nbsp;
However, the need for an uncertainty estimator limits the selection of a surrogate model. In this paper, a Sequential Ensemble Optimization (SEO) algorithm based on the ensemble model is proposed.  ...  The Efficient Global Optimization (EGO) algorithm has been widely used in the numerical design optimization of engineering systems.  ...  Sequential ensemble optimization algorithm As mentioned above, the proposed SEO algorithm is developed based on the work by Jones et al. 1 in extending the EGO algorithm so that it can be used based  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cja.2021.03.010">doi:10.1016/j.cja.2021.03.010</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ava5lyxbuzbrhobellfkp5ibyq">fatcat:ava5lyxbuzbrhobellfkp5ibyq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210529102720/https://pdf.sciencedirectassets.com/274151/AIP/1-s2.0-S1000936121001011/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMr%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJIMEYCIQC%2F4DRXfmOVGApz5ODJLSbi3arDnd0NqUZF%2Fp6B725scQIhAMTVSiHKOfGi%2F79GOjyYYoxtTrkOF9ZoLkazrd01leJtKvoDCHIQBBoMMDU5MDAzNTQ2ODY1IgyOF9rMCVQWg8p6ySQq1wOWVuPz1jKCxgyV4yBeYTB4jtOy5UhFpHfsO2gaJRVUsOrWYCuelKfX8qIFxZDbMzxrYCXsPMCfpqJBvSmlbPjmEEqrtki0aaiabUvXGlAG%2B3mkVPg7yAn%2BsturD16Si%2BXGTh58o89u0gUQNxkFazpWN4hb7f%2F1u8UhZsypVK7LW7cXYt%2BH7h7ZHriPp8o%2F1uGYlvmgDdBtCkdgeTD9c4IfMfTOE%2FCpNfoYDHWmV7HVf2WmoAnexsmcMfG5pKv3lGS%2BQUqZ7mRsSCAjCud1vCMpt8MMUzgmg5ottO5UcxLGyh1rujZFnwMAVFhzAxVwKEfyoDuFNP1OpPcwqVet8y9zL998z5L3K57oloXBVCVvB2C9ueYoOCxIs%2FB4xsRzt9ojMsex8tj11DsWOoygskdQBEz2UNhP4%2BJvMIZB0l%2Fk%2BLWUoi8P9WIkB2J%2FiVC%2B%2Fz0Y5RVMZobyrYw7Qt05e2CkEFgme3SRLSeOBPvHQ4%2FD7DRVV7TGfLDjiUb3tPjJNBEtGgYmuSo5XuCZe2LNUF2icM%2BWguYdysA44Q9dc8S62MUfe4iJWZ75zNMWbbAkHUq6muwAGAVVAS%2FG2rIxz49BHW4tSYjG56tT4TR%2FeR6DYuycVJXZP4AwlY%2FIhQY6pAEfmPHpGVVLI197TcSfPZ1wH3WbD4nxQAoRWXVm6Qf6Pxlhm4YyYhHkCTMf1ABEST%2B41SftWl6k5NLicEUgbYcJh3c3Ar0YhnFWFe%2B7iTzCXgBoyzUfQjqlhFeVE6ka%2B91V%2By9l1DNfMlzA5j8FzJR7dyMKFpWbU48kzz0AsvZDHOK8dMKw6sLI8EOSbo4VA9lMSIzTYFenLBauoXbeStdrTSt7tQ%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210529T102713Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYTQNBWLGY%2F20210529%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=b42596c8c8a045f76af3cfc13e30a1f37e9df5949b2f7d78428933e8be727e1e&amp;hash=16fdd3229740aef6546ee7934d6d96907f2fb607c90c42ec562fc63591bf5b1e&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1000936121001011&amp;tid=spdf-6b5f113c-9723-42cb-b70f-6a366751c15c&amp;sid=a26fd26114df804070880a74fdb21f745f63gxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/60/ac608eae5385eca68336ad7b017b19a1658412a8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cja.2021.03.010"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

A novel sequential design strategy for global surrogate modeling

Karel Crombecq, Luciano De Tommasi, Dirk Gorissen, Tom Dhaene
<span title="">2009</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7n2gtrr2tzevzonkaymviwxhsa" style="color: black;">Proceedings of the 2009 Winter Simulation Conference (WSC)</a> </i> &nbsp;
Sequential design methods are iterative algorithms that use data acquired from previous iterations to guide future sample selection.  ...  In this paper, a comparison is made between different sequential design methods for global surrogate modeling on a real-world electronics problem.  ...  These optimization algorithms may also employ sequential design techniques to minimize the number of samples required to find the global optimum.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wsc.2009.5429687">doi:10.1109/wsc.2009.5429687</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsc/CrombecqGTD09.html">dblp:conf/wsc/CrombecqGTD09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dozo4yn2jvg2nfgui7a3cjgokm">fatcat:dozo4yn2jvg2nfgui7a3cjgokm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812042749/http://www.informs-sim.org/wsc09papers/071.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/65/26/65262ae3c7ab845f455d382fdb723eff57ddb125.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wsc.2009.5429687"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Fuzzy local linear approximation-based sequential design

Joachim van der Herten, Dirk Deschrijver, Tom Dhaene
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gcoazle6pzggzosflq3hpuuoom" style="color: black;">2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)</a> </i> &nbsp;
LOLA-Voronoi, a powerful state of the art method for sequential design combines an Exploitation and Exploration algorithm and adapts the sampling distribution to provide extra samples in non-linear regions  ...  In this paper, a new gradient estimation approach for the LOLA algorithm is proposed based on Fuzzy Logic.  ...  In local surrogate modeling, local models are used to guide the optimization algorithm towards a global optimum. The local models are discarded afterwards.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cies.2014.7011825">doi:10.1109/cies.2014.7011825</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cies/HertenDD14.html">dblp:conf/cies/HertenDD14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/osj6els3zneoze27ebcctgxd6u">fatcat:osj6els3zneoze27ebcctgxd6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724074836/https://biblio.ugent.be/publication/5875648/file/5875652.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/06/fa06f9fbb7334da4bac6495f594c4746266cb9d0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cies.2014.7011825"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A balanced sequential design strategy for global surrogate modeling

Prashant Singh, Dirk Deschrijver, Tom Dhaene
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7n2gtrr2tzevzonkaymviwxhsa" style="color: black;">2013 Winter Simulations Conference (WSC)</a> </i> &nbsp;
The sequential design methodology for global surrogate modeling of complex systems consists of iteratively training the model on a growing set of samples.  ...  Sample selection is a critical step in the process and influences the final quality of the model.  ...  Such surrogates are called global surrogate models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wsc.2013.6721594">doi:10.1109/wsc.2013.6721594</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsc/SinghDD13.html">dblp:conf/wsc/SinghDD13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xv6pm7yrujf6rndrp4jxwwksce">fatcat:xv6pm7yrujf6rndrp4jxwwksce</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150316122146/http://informs-sim.org/wsc13papers/includes/files/191.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/82/f3/82f33e2d9ef16e01e45adf404928a29efe934c0d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wsc.2013.6721594"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A surrogate-based cooperative optimization framework for computationally expensive black-box problems

José Carlos García-García, Ricardo García-Ródenas, Esteve Codina
<span title="2020-07-02">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yj4ipyp3l5hpnkze4vegfei4lm" style="color: black;">Optimization and Engineering</a> </i> &nbsp;
Each algorithm of this class is called a Sequential Multipoint Infill Sampling Algorithm (SMISA) and is the combination resulting from choosing a surrogate model, an exploitation measure, an exploration  ...  Firstly, a class of parallel surrogate-based optimization algorithms is developed, based on the idea of viewing the infill sampling criterion as a bi-objective optimization problem.  ...  Definition 3 (SMISA) A Sequential Multi-point Infill Sampling Algorithm (SMISA) is any algorithm of the parallel surrogate-based optimization framework given in Fig. 1 in which the infill sampling criterion  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11081-020-09526-7">doi:10.1007/s11081-020-09526-7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3qlgygirprafhdif53essozqu4">fatcat:3qlgygirprafhdif53essozqu4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108082315/https://link.springer.com/content/pdf/10.1007/s11081-020-09526-7.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/82/e682b725b4cc06f2f9ba1448704cc4d7818b043d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11081-020-09526-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Modified Sequential Kriging Optimization for Multidisciplinary Complex Product Simulation

Wang Hao, Wang Shaoping, Mileta M. Tomovic
<span title="">2010</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7j7rymwfdvdgha64ol5ajggjqa" style="color: black;">Chinese Journal of Aeronautics</a> </i> &nbsp;
The example shows that MSKO can approach the global optimization quickly and accurately. MSKO can ensure global optimization no matter where the initial point is.  ...  Since the sequential Kriging optimization is time consuming, this article extends the expected improvement and put forwards a modified sequential Kriging optimization (MSKO).  ...  In former research, sequential Kriging optimization (SKO) algorithm is used to find the best solution based on Kriging surrogate model while it is time consuming due to its sampling rule, in which only  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1000-9361(09)60262-4">doi:10.1016/s1000-9361(09)60262-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gmtdbdy4frev7g2zrvsq66ra4y">fatcat:gmtdbdy4frev7g2zrvsq66ra4y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190319174447/https://core.ac.uk/download/pdf/82727686.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/86/5e8620fe0229be24e56a111fdfb7c06586b939d6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/s1000-9361(09)60262-4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Efficient Design Optimization Assisted by Sequential Surrogate Models

Emiliano Iuliano
<span title="2019-05-12">2019</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/75xpl4z7arculfjaok5ombj2ju" style="color: black;">International Journal of Aerospace Engineering</a> </i> &nbsp;
The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill criteria.  ...  Sequential design is achieved by introducing several infill criteria according to the realization of the exploration-exploitation trade-off.  ...  Surrogate-Based Sequential Optimization The workflow of the surrogate-based optimization is depicted in Figure 7 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/4937261">doi:10.1155/2019/4937261</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7qgilmwtafhchcrawozthsuhma">fatcat:7qgilmwtafhchcrawozthsuhma</a> </span>
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PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection [chapter]

Stefan Hess, Tobias Wagner, Bernd Bischl
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Therefore, we propose a new ensemble-based approach which is capable of identifying the best surrogate model during the optimization process by using reinforcement learning techniques.  ...  The procedure is general and can be applied to arbitrary ensembles of surrogate models.  ...  Acknowledgements This paper is based on investigations of the project D5 of the Collaborative Research Center SFB/TR TRR 30 and of the project C2 of the Collaborative Research Center SFB 823, which are  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-44973-4_13">doi:10.1007/978-3-642-44973-4_13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/doz2wrmfgjbbnpbvkawwnqo75q">fatcat:doz2wrmfgjbbnpbvkawwnqo75q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810131942/http://intelligent-optimization.org/LION7/Hess-Wagner-Bischl_PROGRESS_2012.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/7d/9a7db0622613c81301e8b62cde459b77cd8f2332.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-44973-4_13"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Surrogate Modelling with Sequential Design for Expensive Simulation Applications [chapter]

Joachim van der Herten, Tom Van Steenkiste, Ivo Couckuyt, Tom Dhaene
<span title="2017-06-07">2017</span> <i title="InTech"> Computer Simulation </i> &nbsp;
Extended with sequential design, satisfactory solutions can be identified quickly, greatly motivating the adoption of this technology into the design process.  ...  Surrogate models provide an appealing data-driven strategy to accomplish these goals for applications including design space exploration, optimization, visualization or sensitivity analysis.  ...  Surrogate Modelling with Sequential Design for Expensive Simulation Applications http://dx.doi.org/10.5772/67739 Roughly, all methods for adaptive sampling are based on any of the following criteria  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/67739">doi:10.5772/67739</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/35fve7ysyzef5cbixpgjid2m5m">fatcat:35fve7ysyzef5cbixpgjid2m5m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428032205/https://biblio.ugent.be/publication/8577294/file/8577295.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b1/a3/b1a3f8aad894147d69b36aa3ce853dd13564101f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/67739"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments

Amir Parnianifard, Ratchatin Chancharoen, Gridsada Phanomchoeng, Lunchakorn Wuttisittikulkij
<span title="">2020</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wjdkfhqywrdefeqoyzjz3os76i" style="color: black;">International Journal of Computational Intelligence Systems</a> </i> &nbsp;
This paper presents an adaptive algorithm called the Surrogate-Based Constrained Global-Optimization (SCGO) method to solve black-box constrained simulation-based optimization problems involving computationally  ...  Then, an adaptive approach is provided to improve the optimal results sequentially while enforcing a feasible solution.  ...  Figure 1 1 The procedure of proposed Surrogate-Based Constrained Global-Optimization (SCGO) approach. Algorithm 1 : 1 Proposed SCGO algorithm in pseudocode.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/ijcis.d.201014.001">doi:10.2991/ijcis.d.201014.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pi6xj6jcmnhnpdpnnx5r7ryo2y">fatcat:pi6xj6jcmnhnpdpnnx5r7ryo2y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201212024534/https://www.atlantis-press.com/article/125945411.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/29/52293a65d68ee6ce1edbedfb45edc955250fba20.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/ijcis.d.201014.001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Fast optimization of microwave filters using surrogate-based optimization methods

Krishnan Chemmangat, Dirk Deschrijver, Ivo Couckuyt, Tom Dhaene, Luc Knockaert
<span title="">2012</span> <i title="IEEE"> 2012 International Conference on Electromagnetics in Advanced Applications </i> &nbsp;
Based on these data samples, successive global surrogate models are built that become increasingly accurate near the optimum solution.  ...  This paper investigates the use of surrogate-based optimization to optimize the behavioral response of broadband microwave filters.  ...  The Expected Improvement (EI) infill criterion is then used to select additional data samples in a sequential way, and the Kriging surrogate model is updated (Sect. 2.4).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iceaa.2012.6328623">doi:10.1109/iceaa.2012.6328623</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lycdthsktfekhgxr7u2w6ubtc4">fatcat:lycdthsktfekhgxr7u2w6ubtc4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106125029/http://sumo.intec.ugent.be/sites/sumo/files/sumo/2012_09__IEEE_ICEAA.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/fa/f6fa3fb64397d439fa279003cf1199c707605bd5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iceaa.2012.6328623"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Universal Prediction Distribution for Surrogate Models

Malek Ben Salem, Olivier Roustant, Fabrice Gamboa, Lionel Tomaso
<span title="">2017</span> <i title="Society for Industrial &amp; Applied Mathematics (SIAM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/urysiflaw5ezrk6feu5kukoqhq" style="color: black;">SIAM/ASA Journal on Uncertainty Quantification</a> </i> &nbsp;
We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm.  ...  Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.  ...  There are two ways to sample: either drawing the training set (x j ) 1≤j≤n at once or building it sequentially. Among the sequential techniques, some are based on surrogate models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/15m1053529">doi:10.1137/15m1053529</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lttctr2x3rb43bncp6hvxgjqim">fatcat:lttctr2x3rb43bncp6hvxgjqim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812113829/https://hal-emse.ccsd.cnrs.fr/emse-01239789/file/main.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/7c/fb7c0c449e5e17ea606c7c3fb19c471c773d78f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/15m1053529"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Universal Prediction Distribution for Surrogate Models [article]

Malek Ben Salem, Fabrice Gamboa, Lionel Tomaso
<span title="2015-12-23">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm.  ...  Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.  ...  There are two ways to sample: either drawing the training set (x j ) 1≤j≤n at once or building it sequentially. Among the sequential techniques, some are based on surrogate models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1512.07560v1">arXiv:1512.07560v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eyraj4nz7vgvfh2byebi76raue">fatcat:eyraj4nz7vgvfh2byebi76raue</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200916034942/https://arxiv.org/pdf/1512.07560v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/38/fa38a883ed265300cc282b8f467831728978232a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1512.07560v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Sequential Optimization of Strip Bending Process Using Multiquadric Radial Basis Function Surrogate Models

Jos Havinga, Gerrit Klaseboer, A.H. van den Boogaard
<span title="">2013</span> <i title="Trans Tech Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ejbig23ibjcmjfte2hgpbtosf4" style="color: black;">Key Engineering Materials</a> </i> &nbsp;
Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region.  ...  In this paper the deteriorating global behavior of the Kriging surrogate modeling technique is shown for a model of a strip bending process.  ...  Hence, it may be difficult for the sequential optimization algorithm to distinguish which of the local minima is the global minimum.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4028/www.scientific.net/kem.554-557.911">doi:10.4028/www.scientific.net/kem.554-557.911</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tkugfyulejfyhl5bbw7dtmzdx4">fatcat:tkugfyulejfyhl5bbw7dtmzdx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719092103/https://ris.utwente.nl/ws/files/5505815/Esaform2013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cc/48/cc4821eb969610c682d602b7c4de08116d4fde6f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4028/www.scientific.net/kem.554-557.911"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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