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Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain

Yves Caseau (2006)

RAIRO - Operations Research

This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy...

Des explications pour reconnaître et exploiter les structures cachées d’un problème combinatoire

Hadrien Cambazard, Narendra Jussien (2006)

RAIRO - Operations Research - Recherche Opérationnelle

L’identification de structures propres à un problème est souvent une étape clef pour la conception d’heuristiques de recherche comme pour la compréhension de la complexité du problème. De nombreuses approches en Recherche Opérationnelle emploient des stratégies de relaxation ou de décomposition dès lors que certaines struc- tures idoines ont été identifiées. L’étape suivante est la conception d’algorithmes de résolution qui puissent intégrer à la volée, pendant la résolution, ce type d’information....

Des explications pour reconnaître et exploiter les structures cachées d'un problème combinatoire

Hadrien Cambazard, Narendra Jussien (2007)

RAIRO - Operations Research

L'identification de structures propres à un problème est souvent une étape clef pour la conception d'heuristiques de recherche comme pour la compréhension de la complexité du problème. De nombreuses approches en Recherche Opérationnelle emploient des stratégies de relaxation ou de décomposition dès lors que certaines struc- tures idoines ont été identifiées. L'étape suivante est la conception d'algorithmes de résolution qui puissent intégrer à la volée, pendant la résolution, ce type d'information....

Discrete-time symmetric polynomial equations with complex coefficients

Didier Henrion, Jan Ježek, Michael Šebek (2002)

Kybernetika

Discrete-time symmetric polynomial equations with complex coefficients are studied in the scalar and matrix case. New theoretical results are derived and several algorithms are proposed and evaluated. Polynomial reduction algorithms are first described to study theoretical properties of the equations. Sylvester matrix algorithms are then developed to solve numerically the equations. The algorithms are implemented in the Polynomial Toolbox for Matlab.

Distributed fuzzy decision making for production scheduling.

Thomas A. Runkler, Rudolf Sollacher, Wendelin Reverey (2004)

Mathware and Soft Computing

In production systems, input materials (educts) pass through multiple sequential stages until they become a product. The production stages consist of different machines with various dynamic characteristics. The coupling of those machines is a non-linear distributed system. With a distributed control system based on a multi-agent approach, the production system can achieve (almost) maximum output, where lot size and lot sequence are the most important control variables. In most production processes...

Experiments with variants of ant algorithms.

Thomas Stützle, Sebastian Linke (2002)

Mathware and Soft Computing

A number of extensions of Ant System, the first ant colony optimization (ACO) algorithm, were proposed in the literature. These extensions typically achieve much improved computational results when compared to the original Ant System. However, many design choices of Ant System are left untouched including the fact that solutions are constructed, that real-numbers are used to simulate pheromone trails, and that explicit pheromone evaporation is used. In this article we experimentally investigate...

Exploiting Tree Decomposition for Guiding Neighborhoods Exploration for VNS

Mathieu Fontaine, Samir Loudni, Patrice Boizumault (2013)

RAIRO - Operations Research - Recherche Opérationnelle

Tree decomposition introduced by Robertson and Seymour aims to decompose a problem into clusters constituting an acyclic graph. There are works exploiting tree decomposition for complete search methods. In this paper, we show how tree decomposition can be used to efficiently guide local search methods that use large neighborhoods like VNS. We propose DGVNS (Decomposition Guided VNS) which uses the graph of clusters in order to build neighborhood structures enabling better diversification and intensification....

Fuzzy multicriteria decision making applied to the strategic plan of Valencia.

Ana M. Nieto Morote, Francisco Ruz Vila (2002)

Mathware and Soft Computing

We present a fuzzy multicriteria decision making to get the ranking of several projects presented to the major council of Valencia whose final aim is to define the future urbanistic structure of the city. This technique allows us to deal with such problems that are defined by linguistic (and vague) terms, like the case mentioned below.

Influence of modeling structure in probabilistic sequential decision problems

Florent Teichteil-Königsbuch, Patrick Fabiani (2006)

RAIRO - Operations Research

Markov Decision Processes (MDPs) are a classical framework for stochastic sequential decision problems, based on an enumerated state space representation. More compact and structured representations have been proposed: factorization techniques use state variables representations, while decomposition techniques are based on a partition of the state space into sub-regions and take advantage of the resulting structure of the state transition graph. We use a family of probabilistic exploration-like...

Interpretable random forest model for identification of edge 3-uncolorable cubic graphs

Adam Dudáš, Bianka Modrovičová (2023)

Kybernetika

Random forest is an ensemble method of machine learning that reaches a high level of accuracy in decision-making but is difficult to understand from the point of view of interpreting local or global decisions. In the article, we use this method as a means to analyze the edge 3-colorability of cubic graphs and to find the properties of the graphs that affect it most strongly. The main contributions of the presented research are four original datasets suitable for machine learning methods, a random...

Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Luis M. de Campos, José A. Gámez, José M. Puerta (2002)

Mathware and Soft Computing

The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used.A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied to solve a great variety...

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