Bibliography

Major publications by the team in recent years

• 1O. Abdelkafi, L. Idoumghar, J. Lepagnot.
  
  A Survey on the Metaheuristics Applied to QAP for the Graphics Processing Units, in: Parallel Processing Letters, 2016, vol. 26, n^o 3, pp. 1–20.
• 2A. Bendjoudi, N. Melab, E. Talbi.
  
  FTH-B&B: A Fault-Tolerant HierarchicalBranch and Bound for Large ScaleUnreliable Environments, in: IEEE Trans. Computers, 2014, vol. 63, n^o 9, pp. 2302–2315.
• 3S. Cahon, N. Melab, E. Talbi.
  
  ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics, in: J. Heuristics, 2004, vol. 10, n^o 3, pp. 357–380.
• 4F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.
  
  Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes, in: Evolutionary Computation, 2017, vol. 25, n^o 4.
• 5B. Derbel.
  
  Contributions to single- and multi- objective optimization: towards distributed and autonomous massive optimization, Université de Lille, 2017, HDR dissertation.
• 6B. Derbel, A. Liefooghe, Q. Zhang, H. Aguirre, K. Tanaka.
  
  Multi-objective Local Search Based on Decomposition, in: Parallel Problem Solving from Nature - PPSN XIV - 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, 2016, pp. 431–441.
• 7J. Gmys, M. Mezmaz, N. Melab, D. Tuyttens.
  
  IVM-based parallel branch-and-bound using hierarchical work stealing on multi-GPU systems, in: Concurrency and Computation: Practice and Experience, 2017, vol. 29, n^o 9.
• 8A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.
  
  Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes, in: Evolutionary Computation in Combinatorial Optimization - 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, 2017, pp. 215–232.
• 9T. V. Luong, N. Melab, E. Talbi.
  
  GPU Computing for Parallel Local Search Metaheuristic Algorithms, in: IEEE Trans. Computers, 2013, vol. 62, n^o 1, pp. 173–185.
• 10A. Nakib, S. Ouchraa, N. Shvai, L. Souquet, E. Talbi.
  
  Deterministic metaheuristic based on fractal decomposition for large-scale optimization, in: Appl. Soft Comput., 2017, vol. 61, pp. 468–485.

Publications of the year

Articles in International Peer-Reviewed Journals

• 11J. S. Almeida, P. P. Rebouças Filho, T. Carneiro, W. Wei, R. Damaševičius, R. Maskeliūnas, V. H. C. de Albuquerque.
  
  Detecting Parkinson's Disease with Sustained Phonation and Speech Signals using Machine Learning Techniques, in: Pattern Recognition Letters, July 2019, vol. 125, pp. 55-62. [ DOI : 10.1016/j.patrec.2019.04.005 ]
  
  https://hal.archives-ouvertes.fr/hal-02380596
• 12L. Asli, M. Aïder, E.-G. Talbi.
  
  Solving a dynamic combinatorial auctions problem by a hybrid metaheuristic based on a fuzzy dominance relation, in: RAIRO - Operations Research, January 2019, vol. 53, n^o 1, pp. 207-221. [ DOI : 10.1051/ro/2018051 ]
  
  https://hal.archives-ouvertes.fr/hal-02304722
• 13T. Carneiro, J. Gmys, N. Melab, D. Tuyttens.
  
  Towards ultra-scale Branch-and-Bound using a high-productivity language, in: Future Generation Computer Systems, November 2019. [ DOI : 10.1016/j.future.2019.11.011 ]
  
  https://hal.archives-ouvertes.fr/hal-02371238
• 14N. Dupin, E.-G. Talbi.
  
  Parallel matheuristics for the discrete unit commitment problem with min-stop ramping constraints, in: International Transactions in Operational Research, January 2020, vol. 27, n^o 1, pp. 219-244. [ DOI : 10.1111/itor.12557 ]
  
  https://hal.archives-ouvertes.fr/hal-02304758
• 15J. Gmys, M. Mezmaz, N. Melab, D. Tuyttens.
  
  A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem, in: European Journal of Operational Research, 2020, forthcoming.
  
  https://hal.inria.fr/hal-02421229
• 16A. Liefooghe, F. Daolio, S. Verel, B. Derbel, H. Aguirre, K. Tanaka.
  
  Landscape-aware performance prediction for evolutionary multi-objective optimization, in: IEEE Transactions on Evolutionary Computation, 2019, forthcoming. [ DOI : 10.1109/TEVC.2019.2940828 ]
  
  https://hal.archives-ouvertes.fr/hal-02294201
• 17A. Nakib, L. Souquet, E.-G. Talbi.
  
  Parallel fractal decomposition based algorithm for big continuous optimization problems, in: Journal of Parallel and Distributed Computing, November 2019, vol. 133, pp. 297-306. [ DOI : 10.1016/j.jpdc.2018.06.002 ]
  
  https://hal.archives-ouvertes.fr/hal-02304882
• 18J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
  
  Efficient global optimization of constrained mixed variable problems, in: Journal of Global Optimization, March 2019, vol. 73, n^o 3, pp. 583-613. [ DOI : 10.1007/s10898-018-0715-1 ]
  
  https://hal.archives-ouvertes.fr/hal-02304730
• 19O. Schutze, C. Hernandez, E.-G. Talbi, J.-Q. Sun, Y. Naranjani, F.-R. Xiong.
  
  Archivers for the representation of the set of approximate solutions for MOPs, in: Journal of Heuristics, February 2019, vol. 25, n^o 1, pp. 71-105. [ DOI : 10.1007/s10732-018-9383-z ]
  
  https://hal.archives-ouvertes.fr/hal-02304717
• 20E.-G. Talbi.
  
  A unified view of parallel multi-objective evolutionary algorithms, in: Journal of Parallel and Distributed Computing, November 2019, vol. 133, pp. 349-358. [ DOI : 10.1016/j.jpdc.2018.04.012 ]
  
  https://hal.archives-ouvertes.fr/hal-02304734
• 21A. Tchernykh, U. Schwiegelsohn, E.-G. Talbi, M. Babenko.
  
  Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability, in: Journal of computational science, September 2019, vol. 36, 100581 p. [ DOI : 10.1016/j.jocs.2016.11.011 ]
  
  https://hal.archives-ouvertes.fr/hal-02304771

International Conferences with Proceedings

• 22O. Abdelkafi, B. Derbel, A. Liefooghe.
  
  A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem, in: IEEE CEC 2019 - IEEE Congress on Evolutionary Computation, Wellington, New Zealand, June 2019.
  
  https://hal.archives-ouvertes.fr/hal-02179193
• 23N. Berveglieri, B. Derbel, A. Liefooghe, H. Aguirre, K. Tanaka.
  
  Surrogate-assisted multiobjective optimization based on decomposition, in: GECCO '19 - Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech Republic, ACM Press, July 2019, pp. 507-515. [ DOI : 10.1145/3321707.3321836 ]
  
  https://hal.archives-ouvertes.fr/hal-02292851
• 24T. Carneiro, N. Melab.
  
  An Incremental Parallel PGAS-based Tree Search Algorithm, in: HPCS 2019 - International Conference on High Performance Computing & Simulation, Dublin, Ireland, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-02170842
• 25T. Carneiro, N. Melab.
  
  Productivity-aware Design and Implementation of Distributed Tree-based Search Algorithms, in: ICCS 2019 - International Conference on Computational Science, Faro, Portugal, June 2019.
  
  https://hal.archives-ouvertes.fr/hal-02139177
• 26B. Derbel, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.
  
  New Features for Continuous Exploratory Landscape Analysis based on the SOO Tree, in: FOGA 2019 - 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms, Potsdam, Germany, ACM Press, August 2019, pp. 72-86.
  
  https://hal.inria.fr/hal-02282986
• 27M. Gobert, J. Gmys, J.-F. Toubeau, F. Vallee, N. Melab, D. Tuyttens.
  
  Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants, in: PaCOS 2019 - International Workshop on the Synergy of Parallel Computing, Optimization and Simulation (part of HPCS 2019), Dublin, Ireland, July 2019.
  
  https://hal.inria.fr/hal-02178314
• 28T. Ito, H. Aguirre, K. Tanaka, A. Liefooghe, B. Derbel, S. Verel.
  
  Estimating Relevance of Variables for Effective Recombination, in: EMO 2019 - International Conference on Evolutionary Multi-Criterion Optimization, East Lansing, Michigan, United States, February 2019, pp. 411-423. [ DOI : 10.1007/978-3-030-12598-1_33 ]
  
  https://hal.archives-ouvertes.fr/hal-02064547
• 30H. Monzón, H. Aguirre, S. Verel, A. Liefooghe, B. Derbel, K. Tanaka.
  
  Dynamic compartmental models for algorithm analysis and population size estimation, in: Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic, Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), ACM Press, July 2019, pp. 2044-2047. [ DOI : 10.1145/3319619.3326912 ]
  
  https://hal.archives-ouvertes.fr/hal-02436226
• 31J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
  
  Surrogate model based optimization of constrained mixed variable problems: application to the design of a launch vehicle thrust frame, in: SciTech 2019 - AIAA Science and Technology Forum and Exposition, San Diego, United States, American Institute of Aeronautics and Astronautics, January 2019. [ DOI : 10.2514/6.2019-1971 ]
  
  https://hal.archives-ouvertes.fr/hal-02304816
• 32L. Souquet, A. Nakib, E.-G. Talbi.
  
  Deterministic multi-objective fractal decomposition algorithm, in: MIC 2019 - 13th Metaheuristics International Conference, Cartagena, Colombia, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-02304975

National Conferences with Proceedings

• 33D. Delabroye, S. Delamare, D. Loup, L. Nussbaum.
  
  Remplacer un routeur par un serveur Linux : retour d'expérience des passerelles d'accès à Grid'5000, in: JRES - Journées Réseaux de l'Enseignement et de la Recherche, Dijon, France, December 2019.
  
  https://hal.inria.fr/hal-02401684

Scientific Books (or Scientific Book chapters)

• 34T. Bartz-Beielstein, B. Filipič, P. Korošec, E.-G. Talbi.
  
  High-Performance Simulation-Based Optimization, Springer, 2020. [ DOI : 10.1007/978-3-030-18764-4 ]
  
  https://hal.archives-ouvertes.fr/hal-02304686
• 35N. Dupin, F. Nielsen, E.-G. Talbi.
  
  K-Medoids Clustering Is Solvable in Polynomial Time for a 2d Pareto Front, in: Optimization of Complex Systems: Theory, Models, Algorithms and Applications, Springer, June 2020, pp. 790-799. [ DOI : 10.1007/978-3-030-21803-4_79 ]
  
  https://hal.archives-ouvertes.fr/hal-02304806
• 36A. Liefooghe, L. Paquete.
  
  Proceedings of the 19th European conference on evolutionary computation in combinatorial optimization (EvoCOP 2019), Lecture Notes in Computer Science, Springer, 2019, vol. 11452. [ DOI : 10.1007/978-3-030-16711-0 ]
  
  https://hal.archives-ouvertes.fr/hal-02292912
• 37N. Melab, J. Gmys, M. Mezmaz, D. Tuyttens.
  
  Many-core Branch-and-Bound for GPU accelerators and MIC coprocessors, in: High-Performance Simulation-Based Optimization, T. Bartz-Beielstein, B. Filipič, P. Korošec, E.-G. Talbi (editors), Studies in Computational Intelligence, Springer, June 2019, vol. 833, 16 p.
  
  https://hal.inria.fr/hal-01924766
• 38J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.
  
  Overview and Comparison of Gaussian Process-Based Surrogate Models for Mixed Continuous and Discrete Variables: Application on Aerospace Design Problems, in: High-Performance Simulation-Based Optimization, Springer, June 2020, pp. 189-224. [ DOI : 10.1007/978-3-030-18764-4_9 ]
  
  https://hal.archives-ouvertes.fr/hal-02304707

References in notes

• 39M. Balesdent, L. Brévault, N. B. Price, S. Defoort, R. Le Riche, N.-H. Kim, R. T. Haftka, N. Bérend.
  
  Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties, in: Space Engineering: Modeling and Optimization with Case Studies, G. Fasano, J. D. Pintér (editors), Springer International Publishing, 2016, pp. 1–48.
  
  http://dx.doi.org/10.1007/978-3-319-41508-6_1
• 40B. Derbel, D. Brockhoff, A. Liefooghe, S. Verel.
  
  On the Impact of Multiobjective Scalarizing Functions, in: Parallel Problem Solving from Nature - PPSN XIII - 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, 2014, pp. 548–558.
• 41B. Derbel, A. Liefooghe, G. Marquet, E. Talbi.
  
  A fine-grained message passing MOEA/D, in: IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, May 25-28, 2015, 2015, pp. 1837–1844.
• 42R. Haftka, D. Villanueva, A. Chaudhuri.
  
  Parallel surrogate-assisted global optimization with expensive functions – a survey, in: Structural and Multidisciplinary Optimization, 2016, vol. 54(1), pp. 3–13.
• 43D. Jones, M. Schonlau, W. Welch.
  
  Efficient Global Optimization of Expensive Black-Box Functions, in: Journal of Global Optimization, 1998, vol. 13(4), pp. 455–492.
• 44J. Pelamatti, L. Brevault, M. Balesdent, E.-G. Talbi, Y. Guerin.
  
  How to deal with mixed-variable optimization problems: An overview of algorithms and formulations, in: Advances in Structural and Multidisciplinary Optimization, Proc. of the 12th World Congress of Structural and Multidisciplinary Optimization (WCSMO12), Springer, 2018, pp. 64–82.
  
  http://dx.doi.org/10.1007/978-3-319-67988-4_5
• 45F. Shahzad, J. Thies, M. Kreutzer, T. Zeiser, G. Hager, G. Wellein.
  
  CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance, in: CoRR, 2017, vol. abs/1708.02030.
  
  http://arxiv.org/abs/1708.02030
• 46N. Shavit.
  
  Data Structures in the Multicore Age, in: Communications of the ACM, 2011, vol. 54, n^o 3, pp. 76–84.
• 47M. Snir, al..
  
  Addressing Failures in Exascale Computing, in: Int. J. High Perform. Comput. Appl., May 2014, vol. 28, n^o 2, pp. 129–173.
• 48E.-G. Talbi.
  
  Combining metaheuristics with mathematical programming, constraint programming and machine learning, in: Annals OR, 2016, vol. 240, n^o 1, pp. 171–215.
• 49T. Vu, B. Derbel.
  
  Parallel Branch-and-Bound in multi-core multi-CPU multi-GPU heterogeneous environments, in: Future Generation Comp. Syst., 2016, vol. 56, pp. 95–109.
• 50X. Zhang, Y. Tian, R. Cheng, Y. Jin.
  
  A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization, in: IEEE Trans. Evol. Computation, 2018, vol. 22, n^o 1, pp. 97–112.