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. Vérel, H. E. 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, 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, n^o 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, 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

• 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, n^o 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, n^o 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, n^o 3, pp. 76–84.
• 46M. Snir, al..
  
  Addressing Failures in Exascale Computing, in: Int. J. High Perform. Comput. Appl., May 2014, vol. 28, n^o 2, pp. 129–173.
• 47E. Talbi.
  
  Combining metaheuristics with mathematical programming, constraint programming and machine learning, in: Annals OR, 2016, vol. 240, n^o 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, n^o 1, pp. 97–112.