Optimization Using a Modified Second-Order Approach With Evolutionary Enhancement

Hewlett, Joel; Wilamowski, Bogdan M.; Dündar, Günhan

doi:10.1109/tie.2008.927987

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…A framework for data mining of key safety factors from safety-related cases of consumer products is proposed and depicted in Figure 1. Data mining theory, methods and algorithms have been discussed by many authors in the literature (Wilamowski et al, 1999;Wilamowski and Kaynak, 2000;Li and Xu, 2001;Li et al, 2003Li et al, , 2009Li et al, , 2013aLi et al, , 2013bDuan et al, 2007Duan et al, ,2009Shi et al, 2007;Hewlett et al, 2008;Xu et al, 2008;Wilamowski, 2010;Duan and Xu, 2012;Fritzsche et al, 2012;Hunter et al, 2012;Ingvaldsen and Gulla, 2012;Xu et al, 2012;Yang et al, 2012;Yu et al, 2012;Zhang et al, 2012;Bulysheva and Bulyshev, 2013;Katayev et al, 2013;Wang et al, 2013b;Xia et al, 2013;Xing et al, 2013;Zeng et al, 2013aZeng et al, , 2013b. The functional modules in the framework are classified into three phases, that is, cases collection, extraction of impact factors and retrieving of key factors by knowledge reasoning based on the Bayesian network.…”

Section: A Data Mining Frameworkmentioning

confidence: 99%

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Pan

Wang

et al. 2014

Syst. Res.

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Consumer product safety closely relates to consumer health. In this paper, a knowledge engineering framework is proposed for data mining to identify key safety factors from a large number of consumer product safety cases. Data mining in the framework is performed in three steps. The first step is to collect consumer product safety cases, a case can be semistructured or unstructured, and cases can be collected either manually or automatically by a web spider crawling certain websites. The second step is to extract all safety factors from a number of consumer product safety cases. A new method based on linear chain conditional random field is developed to extract safety factors. The effectiveness of the method has been validated on product cases. The third step is to identify a set of key factors from all safety factors by knowledge reasoning. To illustrate the process of knowledge reasoning, a set of 3192 safety cases of electric products with electric shock accidents is chosen as the case study; a Bayesian network based model is developed to retrieve key safety factors relating to electric shock accidents. The performance of the reasoning model has been verified by a combination of experts' evaluation and experiments, and it has shown the proposed reasoning model can help identify key safety factors of electric shock accidents successfully. Overall, the proposed framework is capable of identifying key safety factors from a large number of consumer product safety cases. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: A Data Mining Frameworkmentioning

confidence: 99%

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Pan

Wang

et al. 2014

Syst. Res.

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“…The gradient has played an important role in the history of search methods [32]. The search space may be viewed as a gradient field [33], and a so-called empirical gradient (EG) can be determined by evaluating the response to the position change especially when the objective function is not be available in a differentiable form at all [5].…”

Section: Search Directionmentioning

confidence: 99%

Two Population-Based Heuristic Search Algorithms and Their Applications

Chen¹,

Dai²,

Zheng³

2011

Search Algorithms and Applications

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“…It is worth noting that the global optimization methods indicated in Table 1 only offer a higher likelihood of capturing the global minimum and often do not guarantee convergence in a general sense. Global optimization methods inspired by the combination of gradient-based methods and heuristic ones have also been proposed (Hewlett et al 2008); however, their suitability for simulation-based design has not been fully investigated yet.…”

Section: A Note On Multi-modalitymentioning

confidence: 99%

An adaptive multi-modal optimization algorithm for simulation-based design of power-electronic circuits

Kobravi

Filizadeh

2009

Engineering Optimization

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An adaptive multi-modal optimization algorithm for simulation-based design of power-electronic circuits, Engineering Optimization, 41:10, 945-969To link to this article: http://dx.This article introduces a non-gradient optimization algorithm for the simulation-based design of nonlinear power-electronic circuits where multiple local minima may exist. The algorithm steers a simultaneous search for multiple optima by generating a multi-resolution mesh in the search space. It adaptively increases its resolution only in regions where the presence of local optima is likely, thus offering significant savings in the number of (simulation-based) objective-function evaluations. The algorithm's relatively low computational intensity, its independence of local gradient information and its capability to discover multiple local minima simultaneously make it appealing for the optimization of expensive black-box functions. The article shows an implementation of the algorithm in MATLAB ® and links it to the PSCAD ® /EMTDC transient simulation program. Examples of the application of the proposed algorithm for both analytical and simulation-based objective functions are included.

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Optimization Using a Modified Second-Order Approach With Evolutionary Enhancement

Cited by 16 publications

References 18 publications

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Two Population-Based Heuristic Search Algorithms and Their Applications

An adaptive multi-modal optimization algorithm for simulation-based design of power-electronic circuits

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