Network Reliability Optimization via the Cross-Entropy Method

Kroese, Dirk P.; Hui, Kin-Ping; Nariai, S.

doi:10.1109/tr.2007.895303

Cited by 56 publications

(33 citation statements)

References 24 publications

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“…A rich literature in DOvS has developed over the last 50 years, and the specific methods [125], wireless sensor networks [49], circuit design [130], network reliability [123] Transportation and logistics Traffic control and simulation [211,17,151], metro/transit travel times [83,148], air traffic control [119,96] …”

Section: Discrete Optimization Via Simulationmentioning

confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

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Section: Discrete Optimization Via Simulationmentioning

confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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“…We now explain the implementation and mathematical details, largely following Kroese et al (2007a). Let G(V , E , K ) be an undirected graph, where V = {1, .…”

Section: Network Planning Problemmentioning

confidence: 99%

“…To date, the CE method has been successfully applied to mixed integer nonlinear programming (Kothari and Kroese 2009); continuous optimal control problems (Sani 2009, Sani andKroese 2008); continuous multi-extremal optimization (Kroese et al 2006); multidimensional independent component analysis (Szabó et al 2006); optimal policy search (Busoniu et al 2010); clustering (Botev and Kroese 2004, Kroese et al 2007b, Boubezoula et al 2008); signal detection (Liu et al 2004); DNA sequence alignment Kroese 2002, Pihur et al 2007); noisy optimization problems such as optimal buffer allocation (Alon et al 2005); resource allocation in stochastic systems (Cohen et al 2007); network reliability optimization (Kroese et al 2007a); vehicle routing optimization with stochastic demands (Chepuri and Homem-de-Mello 2005); power system combinatorial optimization problems (Ernst et al 2007); and neural and reinforcement learning (Lörincza et al 2008, Menache et al 2005, Unveren and Acan 2007, Wu and Fyfe 2008.…”

Section: Introductionmentioning

confidence: 99%

The Cross-Entropy Method for Optimization

Botev

Kroese

Rubinstein

et al. 2013

Handbook of Statistics

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167

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The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback-Leibler (or cross-entropy) minimization. As an optimization method it unifies many existing populationbased optimization heuristics. In this chapter we show how the cross-entropy method can be applied to a diverse range of combinatorial, continuous, and noisy optimization problems.

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“…We are aware of two previous solutions for the general case. One, proposed by Kroese et al, is based on the cross-entropy method [7]. De Raedt et al give other solution to the general case in the setting of theory compression for ProbLog [9,8].…”

Section: Related Workmentioning

confidence: 99%

Fast Discovery of Reliable k-terminal Subgraphs

Kasari

Toivonen

Hintsanen

2010

Advances in Knowledge Discovery and Data Mining

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Abstract. We present a novel and efficient algorithm for solving the most reliable subgraph problem with multiple query nodes on undirected random graphs. Reliable subgraphs are useful for summarizing connectivity between given query nodes. Formally, we are given a graph G = (V, E), a set of query (or terminal) nodes Q ⊂ V , and a positive integer B. The objective is to find a subgraph H ⊂ G containing Q, such that H has at most B edges, and the probability that H is connected is maximized. Previous algorithms for the problem are either computationally demanding, or restricted to only two query nodes. Our algorithm extends a previous algorithm to handle k query nodes, where 2 ≤ k ≤ |V |. We demonstrate experimentally the usefulness of reliable k-terminal subgraphs, and the accuracy, efficiency and scalability of the proposed algorithm on real graphs derived from public biological databases.

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Network Reliability Optimization via the Cross-Entropy Method

Cited by 56 publications

References 24 publications

Simulation optimization: a review of algorithms and applications

Simulation optimization: a review of algorithms and applications

The Cross-Entropy Method for Optimization

Fast Discovery of Reliable k-terminal Subgraphs

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