Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 1J. Gmys.
  
  Heterogeneous cluster computing for many-task exact optimization - Application to permutation problems, Université de Mons (UMONS) ; Université de Lille, December 2017.
  
  https://hal.inria.fr/tel-01652000

Articles in International Peer-Reviewed Journals

• 2E. Alekseeva, M. Mezmaz, D. Tuyttens, N. Melab.
  
  Parallel multi-core hyper-heuristic GRASP to solve permutation flow-shop problem: Hyper-heuristique GRASP parallèle multi-coeur pour la résolution du flow-shop de permutation, in: Concurrency and Computation: Practice and Experience, May 2017, vol. 29, n^o 9, 15 p. [ DOI : 10.1002/cpe.3835 ]
  
  https://hal.inria.fr/hal-01419060
• 3V. N. Coelho, T. A. Oliveira, I. M. Coelho, B. N. Coelho, P. J. Fleming, F. G. Guimarães, H. Ramalhinho, M. J. Souza, E.-G. Talbi, T. Lust.
  
  Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign, in: Computers and Operations Research, February 2017, vol. 78, pp. 578-587. [ DOI : 10.1016/j.cor.2016.09.008 ]
  
  https://hal.inria.fr/hal-01654721
• 4F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, K. Tanaka.
  
  Problem Features vs. Algorithm Performance on Rugged Multi-objective Combinatorial Fitness Landscapes, in: Evolutionary Computation, 2017, vol. 25, n^o 4, pp. 555–585. [ DOI : 10.1162/EVCO_a_00193 ]
  
  https://hal.archives-ouvertes.fr/hal-01380612
• 5J. 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, May 2017, vol. 29, n^o 9. [ DOI : 10.1002/cpe.4019 ]
  
  https://hal.archives-ouvertes.fr/hal-01673376
• 6N. 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, January 2017, 20 p, In Press, corrected proof, forthcoming.
  
  https://hal.inria.fr/hal-01419079
• 7N. Melab, M. Mezmaz.
  
  Multi and many-core computing for parallel metaheuristics, in: Concurrency and Computation: Practice and Experience, May 2017, vol. 29, n^o 9. [ DOI : 10.1002/cpe.4116 ]
  
  https://hal.inria.fr/hal-01648278
• 8A. Nakib, S. Ouchra, S. Chvai, E.-G. Talbi, L. Souquet.
  
  Deterministic metaheuristic based on fractal decomposition for large-scale optimization, in: Applied Soft Computing, December 2017, vol. 61, pp. 468-485.
  
  https://hal.inria.fr/hal-01660190
• 9E.-G. Talbi, E. Alekseeva, Y. Kochetov.
  
  A matheuristic for the discrete bi-level problem with multiple objectives at the lower level, in: International Transactions in Operational Research, 2017, vol. 24, n^o 5, pp. 959-981.
  
  https://hal.inria.fr/hal-01654723
• 10E.-G. Talbi, R. Todosijievic.
  
  The robust uncapacitated multiple allocation p-hub median problem, in: Computers and Industrial Engineering, 2017, vol. 110, pp. 322-332.
  
  https://hal.inria.fr/hal-01654718
• 11C. Tiago, J. Gmys, d. C. J. Francisco Heron, N. Melab, D. Tuyttens.
  
  GPU-accelerated backtracking using CUDA Dynamic Parallelism, in: Concurrency and Computation: Practice and Experience, 2017, forthcoming.
  
  https://hal.inria.fr/hal-01648125
• 12M. Vandromme, J. Jacques, J. Taillard, H. Arnaud, L. Jourdan, C. Dhaenens.
  
  Extraction and optimization of classification rules for temporal sequences: Application to hospital data, in: Knowledge-Based Systems, May 2017, vol. 122, pp. 148-158. [ DOI : 10.1016/j.knosys.2017.02.001 ]
  
  https://hal.archives-ouvertes.fr/hal-01564520

International Conferences with Proceedings

• 13A. Blot, A. Pernet, L. Jourdan, M.-É. Kessaci-Marmion, H. H. Hoos.
  
  Automatically Configuring Multi-objective Local Search Using Multi-objective Optimisation, in: EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Münster, Germany, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (editors), Evolutionary Multi-Criterion Optimization (EMO 2017), Springer, March 2017, vol. 10173, pp. 61-73. [ DOI : 10.1007/978-3-319-54157-0_5 ]
  
  https://hal.archives-ouvertes.fr/hal-01559690
• 14B. Camus, F. Dufossé, A.-C. Orgerie.
  
  A stochastic approach for optimizing green energy consumption in distributed clouds, in: SMARTGREENS 2017 - International Conference on Smart Cities and Green ICT Systems, Porto, Portugal, April 2017.
  
  https://hal.inria.fr/hal-01475431
• 15O. Cuate, E.-G. Talbi, B. Derbel, A. Liefooghe, O. Schütze.
  
  An approach fort he local exploration of discrete many objective optimization, in: EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Münster, Germany, LNCS, March 2017, vol. 10173, pp. 135-150. [ DOI : 10.1007/978-3-319-54157-0_10 ]
  
  https://hal.inria.fr/hal-01654731
• 16N. Dahmani, S. Krichen, E.-G. Talbi.
  
  An exact Epsilon constraint method for solving the multi-objective 2D vector packing problem, in: ICMSAO‘2017 7th Int. Conf. on Modeling Simulation and Applied Optimization, Sharjah, United Arab Emirates, 2017.
  
  https://hal.inria.fr/hal-01654730
• 17Z. Garroussi, R. Ellaia, E.-G. Talbi.
  
  Hybrid Evolutionary Algorithm for Residential Demand Side Management with a Photovoltaic Panel and a Battery, in: ICCAIRO’2017 Conf. on Control, Artificial Intelligence, Robotics & Optimization, prague, Czech Republic, May 2017.
  
  https://hal.inria.fr/hal-01654829
• 18Z. Garroussi, E.-G. Talbi, R. Ellaia.
  
  Hybrid multi-objective evolutionary algorithms for the residential demand side management with thermal and electrical loads, in: MIC’2017 Metaheuristics International Conference, Barcelone, Spain, July 2017.
  
  https://hal.inria.fr/hal-01654818
• 19M. Gonzalez-Rodriguez, J.-J. Lopez-Espin, E.-G. Talbi, J. Aparicio.
  
  A parameterized scheme of metaheuristics with exact methods for determining the principle of least action in Data Envelopment Analysis, in: CEC’2017 Congress of Evolutionary Algortihms, San Sebastien, Spain, June 2017.
  
  https://hal.inria.fr/hal-01654836
• 20A. Liefooghe, B. Derbel, S. Verel, H. Aguirre, K. Tanaka.
  
  A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems, in: 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, H. Trautmann, G. Rudolph, K. Klamroth, O. Schütze, M. Wiecek, Y. Jin, C. Grimme (editors), Evolutionary Multi-Criterion Optimization (EMO 2017), Springer, March 2017, vol. 10173, n^o 422-437. [ DOI : 10.1007/978-3-319-54157-0_29 ]
  
  https://hal.archives-ouvertes.fr/hal-01496357
• 21A. 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 Optimisation (EvoCOP 2017), Amsterdam, Netherlands, B. Hu, M. López-Ibáñez (editors), Evolutionary Computation in Combinatorial Optimization, Springer, April 2017, vol. 10197, pp. 215-232.
  
  https://hal.archives-ouvertes.fr/hal-01496347
• 22H. Monzón, H. Aguirre, S. Verel, A. Liefooghe, B. Derbel, K. Tanaka.
  
  Closed state model for understanding the dynamics of MOEAs, in: Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany, Genetic and Evolutionary Computation Conference (GECCO 2017), ACM, July 2017.
  
  https://hal.archives-ouvertes.fr/hal-01496329
• 23L. Mousin, M.-E. Kessaci, C. Dhaenens.
  
  A new constructive heuristic for the No-Wait Flowshop Scheduling Problem, in: 11th Learning and Intelligent OptimizatioN Conference, Nizhny Novgorod, Russia, June 2017.
  
  https://hal.archives-ouvertes.fr/hal-01579775
• 24A. Nakib, M. Hilia, F. Héliodore, E.-G. Talbi.
  
  Design of metaheuristic based on machine learning: A unified approach, in: IPDPS’2017 Int. Parallel and Distibuted Processing Symposium Workshop, Orlando, United States, May 2017, pp. 510-528.
  
  https://hal.inria.fr/hal-01654860
• 25M. Sagawa, H. Aguirre, F. Daolio, A. Liefooghe, B. Derbel, S. Verel, K. Tanaka.
  
  Learning variable importance to guide recombination on many-objective optimization, in: 5th International Conference on Smart Computing and Artificial Intelligence (SCAI 2017), Hamamatsu, Japan, July 2017.
  
  https://hal.archives-ouvertes.fr/hal-01581247
• 26J. Shi, Q. Zhang, B. Derbel, A. Liefooghe.
  
  A parallel tabu search for the unconstrained binary quadratic programming problem, in: IEEE Congress on Evolutionary Computation (CEC 2017), Donostia - San Sebastián, Spain, June 2017, pp. 557-564.
  
  https://hal.archives-ouvertes.fr/hal-01581361
• 28E.-G. Talbi, N. Dupin.
  
  Matheuristics for a VRPTW with competence constraints, in: MIC’2017 - Metaheuristics International Conference, Barcelone, Spain, July 2017.
  
  https://hal.inria.fr/hal-01654864
• 29E.-G. Talbi, M. Hajjem, H. Bouziri, K. Mellouli.
  
  Intelligent indoor evacuation guidance based on ant colony algorithm, in: IEEE AICCSA’2017 Int. Conf. on Computer Systems and Applications, Hammamet, Tunisia, October 2017.
  
  https://hal.inria.fr/hal-01654880

Conferences without Proceedings

• 30O. ABDELKAFI, L. Idoumghar, J. Lepagnot.
  
  Distance Cooperation between Hybrid Iterative Tabu Search, in: Artificial Evolution EA 2017, Paris, France, October 2017.
  
  https://hal.archives-ouvertes.fr/hal-01672040
• 31A. Blot, M.-É. Kessaci-Marmion, L. Jourdan.
  
  AMH: a new Framework to Design Adaptive Metaheuristics, in: 12th Metaheuristics International Conference, Barcelona, Spain, July 2017.
  
  https://hal.archives-ouvertes.fr/hal-01559687
• 32A. Blot, M.-É. Kessaci-Marmion, L. Jourdan.
  
  AMH: une plate-forme pour le design et le contrôle automatique de métaheuristiques multi-objectif, in: ROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Metz, France, February 2017.
  
  https://hal.archives-ouvertes.fr/hal-01560444
• 33O. Cuate, B. Derbel, A. Liefooghe, E.-G. Talbi, O. Schütze.
  
  An approach for the local exploration of discrete many objective optimization problems, in: 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Münster, Germany, Lecture Notes in Computer Science, March 2017, vol. 10173, pp. 135-150. [ DOI : 10.1109/TSMCB.2008.926329 ]
  
  https://hal.archives-ouvertes.fr/hal-01581433
• 34L. Mousin, M.-E. Kessaci, C. Dhaenens.
  
  De nouvelles meilleures solutions pour le problème d'ordonnancement No-Wait Flowshop, in: ROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Metz, France, February 2017.
  
  https://hal.archives-ouvertes.fr/hal-01579762

Scientific Books (or Scientific Book chapters)

• 35O. Bahri, N. B. Amor, E.-G. Talbi.
  
  Possibilistic framework for multi-objective optimization under uncertainty, in: Recent developments of metaheuristics, Springer, 2017, pp. 27-42.
  
  https://hal.inria.fr/hal-01654714
• 36E.-G. Talbi, A. Nakib.
  
  Metaheuristics for medicine and biology, Springer, 2017.
  
  https://hal.inria.fr/hal-01654686
• 37E.-G. Talbi, F. Yalaoui, L. Amodeo.
  
  Recent developments of metaheuristics, Springer, 2017.
  
  https://hal.inria.fr/hal-01654699

Internal Reports

• 38F. Dufossé, K. Kaya, I. Panagiotas, B. Uçar.
  
  Further notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices, Inria - Research Centre Grenoble – Rhône-Alpes, September 2017, n^o RR-9095.
  
  https://hal.inria.fr/hal-01586245

References in notes

• 39C. A. Coello Coello, D. A. Van Veldhuizen, G. B. Lamont (editors)
  
  Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Press, 2002.
• 40M. Basseur.
  
  Design of cooperative algorithms for multi-objective optimization: Application to the Flow-shop scheduling problem, University of Sciences and Technology of Lille, France, June 2005.
• 41C. Cotta, E.-G. Talbi, E. Alba.
  
  Parallel hybrid approaches, in: Parallel Metaheuristics, USA, J. Wiley and Sons, 2005, pp. 347–370.
• 42K. Deb.
  
  Multi-objective optimization using evolutionary algorithms, John Wiley and sons, 2001.
• 43D. E. Goldberg.
  
  Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Massachusett, USA, 1989.
• 44A. J. Nebro, F. Luna, E.-G. Talbi, E. Alba.
  
  Parallel multi-objective optimization, in: Parallel Metaheuristics, USA, J. Wiley and Sons, 2005, pp. 371–394.
• 45E.-G. Talbi.
  
  A Taxonomy of Hybrid Metaheuristics, in: Journal of Heuristics, 2002, vol. 8, n^o 5, pp. 541–564.