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  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

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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, no 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, no 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, no 3, pp. 357–380.
  • 4F. Daolio, A. Liefooghe, S. Vérel, H. E. Aguirre, K. Tanaka.

    Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes, in: Evolutionary Computation, 2017, vol. 25, no 4.
  • 5B. Derbel.

    Contributions to single- and multi- objective optimization: towards distributed and autonomous massive optimization, in: HDR dissertation, Université de Lille, 2017.
  • 6B. Derbel, A. Liefooghe, Q. Zhang, H. E. 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, no 9.
  • 8A. Liefooghe, B. Derbel, S. Vérel, H. E. 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, no 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

  • 11L. 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, 2018. [ DOI : 10.1051/ro/2018051 ]

    https://hal.inria.fr/hal-01942418
  • 12O. Bahri, E.-G. Talbi, N. Ben Amor.

    A generic fuzzy approach for multi-objective optimization under uncertainty, in: Swarm and Evolutionary Computation, June 2018, vol. 40, pp. 166-183. [ DOI : 10.1016/j.swevo.2018.02.002 ]

    https://hal.inria.fr/hal-01942402
  • 13T. Carneiro Pessoa, J. Gmys, F. Heron De Carvalho Junior, N. Melab, D. Tuyttens.

    GPU-Accelerated Backtracking Using CUDA Dynamic Parallelism, in: Concurrency and Computation: Practice and Experience, May 2018, vol. 30, no 9. [ DOI : 10.1002/cpe.4374 ]

    https://hal.inria.fr/hal-01919514
  • 14N. Dupin, E.-G. Talbi.

    Parallel matheuristics for the discrete unit commitment problem with min-stop ramping constraints, in: International Transactions in Operational Research, 2018.

    https://hal.inria.fr/hal-01942412
  • 15N. Melab, J. Gmys, M. Mezmaz, D. Tuyttens.

    Multi-core versus Many-core Computing for Many-task Branch-and-Bound applied to Big Optimization Problems, in: Future Generation Computer Systems, May 2018, vol. 82, 20 p. [ DOI : 10.1016/j.future.2016.12.039 ]

    https://hal.inria.fr/hal-01419079
  • 16N. Melab, A. Zomaya, I. Chakroun.

    Parallel optimization using/for multi and many-core high performance computing, in: Journal of Parallel and Distributed Computing, February 2018, vol. 112, pp. 109 - 110. [ DOI : 10.1016/j.jpdc.2017.11.011 ]

    https://hal.inria.fr/hal-01924680
  • 17A. Nakib, L. Souquet, E.-G. Talbi.

    Parallel fractal decomposition based algorithm for big continuous optimization problems, in: Journal of Parallel and Distributed Computing, 2018. [ DOI : 10.1016/j.jpdc.2018.06.002 ]

    https://hal.archives-ouvertes.fr/hal-01844420
  • 18O. 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, 2018.

    https://hal.inria.fr/hal-01942424
  • 19E.-G. Talbi.

    A unified view of parallel multi-objective evolutionary algorithms, in: Journal of Parallel and Distributed Computing, May 2018, pp. 1-10. [ DOI : 10.1016/j.jpdc.2018.04.012 ]

    https://hal.inria.fr/hal-01942407

International Conferences with Proceedings

  • 20O. Abdelkafi, L. Idoumghar, J. Lepagnot, J.-L. Paillaud.

    The determination of new stable zeolite frameworks using a parallel hybrid genetic algorithm, in: OLA 2018 - International Workshop on Optimization and Learning: Challenges and Applications, Alicante, Spain, February 2018, pp. 1-2.

    https://hal.archives-ouvertes.fr/hal-01726493
  • 21O. Bahri, E.-G. Talbi.

    Dealing with Epistemic Uncertainty in Multi-objective Optimization: A Survey, in: IPMU 2018 - 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Cadiz, Spain, June 2018, pp. 260-271.

    https://hal.inria.fr/hal-01942474
  • 22G. Briffoteaux, N. Melab, M. Mezmaz, D. Tuyttens.

    An adaptive evolution control based on confident regions for surrogate-assisted optimization, in: HPCS 2018 - International Conference on High Performance Computing & Simulation, Orléans, France, July 2018.

    https://hal.archives-ouvertes.fr/hal-01922708
  • 23T. Carneiro Pessoa, J. Gmys, N. Melab, F. Heron De Carvalho Junior, P. P. P. Rebouças Filho, D. Tuyttens.

    Dynamic Configuration of CUDA Runtime Variables for CDP-based Divide-and-Conquer Algorithms, in: VECPAR 2018 - 13th International Meeting on High Performance Computing for Computational Science, São Pedro, Brazil, September 2018.

    https://hal.inria.fr/hal-01919532
  • 24B. Derbel, A. Liefooghe, Q. Zhang, S. Verel, H. Aguirre, K. Tanaka.

    A set-oriented MOEA/D, in: GECCO 2018 - Genetic and Evolutionary Computation Conference, Kyoto, Japan, ACM Press, July 2018, pp. 617-624. [ DOI : 10.1145/3205455.3205575 ]

    https://hal.archives-ouvertes.fr/hal-01823671
  • 25A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi, N. Melab.

    Efficient Global Optimization using Deep Gaussian Processes, in: CEC 2018 - Congress on Evolutionary Computation, Rio de Janeiro, Brazil, July 2018.

    https://hal.inria.fr/hal-01919795
  • 26A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi, N. Melab.

    Multi-Disciplinary Design Multi-Objective Optimization of Aerospace Vehicles using Surrogate Models, in: OLA 2018 - International Workshop on Optimization and Learning: Challenges and Applications, Alicante, Spain, February 2018.

    https://hal.inria.fr/hal-01942467
  • 27A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.

    Towards Landscape-Aware Automatic Algorithm Configuration: Preliminary Experiments on Neutral and Rugged Landscapes, in: European Conference on Evolutionary Computation in Combinatorial Optimization, Parma, Italy, Lecture Notes in Computer Science, April 2018, vol. 10782, pp. 215-232.

    https://hal.archives-ouvertes.fr/hal-01824982
  • 28A. Liefooghe, B. Derbel, S. Verel, M. López-Ibáñez, H. Aguirre, K. Tanaka.

    On Pareto local optimal solutions networks, in: International Conference on Parallel Problem Solving from Nature (PPSN 2018), Coimbra, Portugal, Lecture Notes in Computer Science, Springer, September 2018, vol. 11102, pp. 232-244. [ DOI : 10.1007/978-3-319-99259-4_19 ]

    https://hal.archives-ouvertes.fr/hal-01823721
  • 29A. Liefooghe, M. López-Ibáñez, L. Paquete, S. Verel.

    Dominance, epsilon, and hypervolume local optimal sets in multi-objective optimization, and how to tell the difference, in: GECCO 2018 - Genetic and Evolutionary Computation Conference, Kyoto, Japan, ACM Press, July 2018, vol. 18, pp. 324-331. [ DOI : 10.1145/3205455.3205572 ]

    https://hal.archives-ouvertes.fr/hal-01823666
  • 30J. Shi, Q. Zhang, B. Derbel, A. Liefooghe, J. Sun.

    Parallel Pareto local search revisited – First experimental results on bi-objective UBQP, in: GECCO 2018 - Genetic and Evolutionary Computation Conference, Kyoto, Japan, ACM Press, July 2018, pp. 753-760. [ DOI : 10.1145/3205455.3205577 ]

    https://hal.archives-ouvertes.fr/hal-01920339
  • 31S. Verel, B. Derbel, A. Liefooghe, H. Aguirre, K. Tanaka.

    A surrogate model based on Walsh decomposition for pseudo-boolean functions, in: International Conference on Parallel Problem Solving from Nature (PPSN 2018), Coimbra, Portugal, Lecture Notes in Computer Science, September 2018, vol. 11102, pp. 181-193. [ DOI : 10.1007/978-3-319-99259-4_15 ]

    https://hal.archives-ouvertes.fr/hal-01823725

Scientific Books (or Scientific Book chapters)

  • 32N. 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. Filipic, P. Korosec, E.-G. Talbi (editors), Springer, 2018.

    https://hal.inria.fr/hal-01924766
  • 33J. 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, Springer, 2018, pp. 64-82. [ DOI : 10.1007/978-3-319-67988-4_5 ]

    https://hal.inria.fr/hal-01690446

Books or Proceedings Editing

  • 34P. Korošec, N. Melab, E.-G. Talbi (editors)

    Bioinspired Optimization Methods and Their Applications, Lecture Notes in Computer Science, Springer, Paris, France, 2018, no 10835, pp. XIII - 333.

    https://hal.inria.fr/hal-01942377
  • 35A. Liefooghe, M. López-Ibáñez (editors)

    Proceedings of the 18th European conference on evolutionary computation in combinatorial optimization (EvoCOP 2018), Lecture Notes in Computer Science, Springer, Parma, Italy, 2018, vol. 10782. [ DOI : 10.1007/978-3-319-77449-7 ]

    https://hal.archives-ouvertes.fr/hal-01920369

Other Publications

  • 36Y. Marca, H. Aguirre, S. Zapotecas-Martínez, A. Liefooghe, S. Verel, B. Derbel, K. Tanaka.

    Pareto dominance-based MOEAs on Problems with Difficult Pareto Set Topologies, July 2018, GECCO 2018 - Genetic and Evolutionary Computation Conference Companion, Poster.

    https://hal.archives-ouvertes.fr/hal-01823715
  • 37H. Monzón, H. Aguirre, S. Verel, A. Liefooghe, B. Derbel, K. Tanaka.

    Studying MOEAs Dynamics and their Performance using a Three Compartmental Model, July 2018, GECCO 2018 - Genetic and Evolutionary Computation Conference Companion, Poster.

    https://hal.archives-ouvertes.fr/hal-01823709
  • 38J. Pelamatti, L. Brévault, M. Balesdent, E.-G. Talbi, Y. Guerin.

    Efficient global optimization of constrained mixed variable problems, December 2018, https://arxiv.org/abs/1806.03975 - working paper or preprint.

    https://hal.inria.fr/hal-01942439
References in notes
  • 39M. Balesdent, L. Brevault, N. Price, S. Defoort, R. Le Riche, N.-H. Kim, R. 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, Modeling and Optimization with Case Studies, Springer Optimization and Its Applications, 2016, vol. 114, pp. 1-48. [ DOI : 10.1007/978-3-319-41508-6_1 ]

    https://hal.archives-ouvertes.fr/hal-01475842
  • 40B. Derbel, D. Brockhoff, A. Liefooghe, S. Vérel.

    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.
  • 44F. 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
  • 45N. Shavit.

    Data Structures in the Multicore Age, in: Communications of the ACM, 2011, vol. 54, no 3, pp. 76–84.
  • 46M. Snir, al..

    Addressing Failures in Exascale Computing, in: Int. J. High Perform. Comput. Appl., May 2014, vol. 28, no 2, pp. 129–173.
  • 47E. Talbi.

    Combining metaheuristics with mathematical programming, constraint programming and machine learning, in: Annals OR, 2016, vol. 240, no 1, pp. 171–215.
  • 48T. 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.
  • 49X. 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, no 1, pp. 97–112.