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Your search keyword '"Université de Lille, Sciences Humaines et Sociales"' showing total 5 results
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5 results on '"Université de Lille, Sciences Humaines et Sociales"'

1. Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem

Author

Saoussen Krichen, Nadia Dahmani, El-Ghazali Talbi, François Clautiaux, Institut Supérieur de Gestion de Tunis [Tunis] (ISG), Université de Tunis, Université de Lille, Sciences et Technologies, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique Fondamentale de Lille (LIFL), and Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, education.field_of_study, Bin packing problem, Population, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Bin, Set (abstract data type), Encoding (memory), Minification, education, Metaheuristic, Finite set, Software, Mathematics
Abstract

International audience; In this paper, a multi-objective 2-dimensional vector packing problem is presented. It consists in packing a set of items, each having two sizes in two independent dimensions, say, a weight and a length into a finite number of bins, while concurrently optimizing three cost functions. The first objective is the minimization of the number of used bins. The second one is the minimization of the maximum length of a bin. The third objective consists in balancing the load overall the bins by minimizing the difference between the maximum length and the minimum length of a bin. Two population-based metaheuristics are performed to tackle this problem. These metaheuristics use different indirect encoding approaches in order to find good permutations of items which are then packed by a separate decoder routine whose parameters are embedded in the solution encoding. It leads to a self-adaptive metaheuristic where the parameters are adjusted during the search process. The performance of these strategies is assessed and compared against benchmarks inspired from the literature.

Published
2014

2. A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

Author

Arnaud Liefooghe, Jin-Kao Hao, Sébastien Verel, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Laboratoire d'Etudes et de Recherche en Informatique d'Angers (LERIA), and Université d'Angers (UA)

Subjects
Mathematical optimization, Unconstrained binary quadratic programming, Computer Science::Neural and Evolutionary Computation, MathematicsofComputing_NUMERICALANALYSIS, Combinatorial optimization problem, Multiobjective combinatorial optimization, Binary quadratic programming, Evolutionary Multiobjective Optimization, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Function (mathematics), Scalarizing function, Tabu search, Multiobjective optimization algorithm, Hybrid Metaheuristic, Quadratic unconstrained binary optimization, Metaheuristic, Software, Mathematics
Abstract

The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQP instances with two and three objectives.

Published
2014

3. A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Author

Laetitia Jourdan, Briseida Sarasola, Enrique Alba, El-Ghazali Talbi, Mostepha Khouadjia, Machine Learning and Optimisation (TAO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Departamento Lenguajes y Ciencias de la Computación [Malaga] (LCC), Universidad de Málaga [Málaga] = University of Málaga [Málaga], Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), and Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec

Subjects
Static routing, Mathematical optimization, 021103 operations research, Process (engineering), Computer science, 0211 other engineering and technologies, Particle swarm optimization, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Set (abstract data type), Vehicle routing problem, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Routing (electronic design automation), Software, Variable neighborhood search
Abstract

International audience; Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.

Published
2012

4. Metaheuristics based de novo protein sequencing: A new approach

Author

El-Ghazali Talbi, Laetitia Jourdan, Jean-Charles Boisson, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0303 health sciences, business.industry, Computer science, 010401 analytical chemistry, Protein database, Genomics, Machine learning, computer.software_genre, 01 natural sciences, 0104 chemical sciences, 03 medical and health sciences, ComputingMethodologies_PATTERNRECOGNITION, Protein sequencing, [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], Genetic algorithm, Local search (optimization), Protein identification, Artificial intelligence, business, Metaheuristic, computer, ComputingMilieux_MISCELLANEOUS, Software, 030304 developmental biology, Sequence (medicine)
Abstract

In this article, a new approach is proposed for the de novo protein sequencing problem. The aim is to find the sequence of an experimental protein from only experimental data, i.e. without databases. To do so, a three-step model called SSO for Shape, Sequence and Order has been designed. No prior knowledge in genomics nor protein databases are used. Here we modelized de novo protein sequencing as a combinatorial optimisation problem and propose cooperative metaheuristics to solve it. Results are assessed on experimental proteins and proved the feasibility of this approach.

Published
2011

5. Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets

Author

Clarisse Dhaenens, Julien Taillard, Julie Jacques, David Delerue, Laetitia Jourdan, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Alicante [Seclin], Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), and Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
021103 operations research, Classification rule mining, business.industry, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, computer.software_genre, Missing data, Machine learning, Imbalanced data, Statistical classification, Dominance (economics), [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Local search (optimization), Data mining, Artificial intelligence, business, computer, ComputingMilieux_MISCELLANEOUS, Software, Partial classification, Mathematics
Abstract

Graphical abstractDisplay Omitted HighlightsFormulation of the classification rule mining problem as a multi-objective problem.Proposal of MOCA-I that deals both with uncertainty, class imbalance and volumetry.Comparison of different MOCA-I based DMLS versions and DMLS 1?* shows better results.Comparison with 13 state-of-the-art classification algorithms.MOCA-I gives shorter and statistically more effective rules than other algorithms. Classification on medical data raises several problems such as class imbalance, double meaning of missing data, volumetry or need of highly interpretable results. In this paper a new algorithm is proposed: MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data), a multi-objective local search algorithm that is conceived to deal with these issues all together. It is based on a new modelization as a Pittsburgh multi-objective partial classification rule mining problem, which is described in the first part of this paper. An existing dominance-based multi-objective local search (DMLS) is modified to deal with this modelization. After experimentally tuning the parameters of MOCA-I and determining which version of DMLS algorithm is the most effective, the obtained MOCA-I version is compared to several state-of-the-art classification algorithms. This comparison is realized on 10 small and middle-sized data sets of literature and 2 real data sets; MOCA-I obtains the best results on the 10 data sets and is statistically better than other approaches on the real data sets.

Published
2015

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