Full list

You can also find them here: hal, dblp

Editorials

1. N. Melab, J. Gmys, P. Korošec and I. Chakroun. Synergy between parallel computing, optimization and simulation. Journal of Computational Science, Elsevier, Vol. 44, article 101168, 2020. DOI: https://doi.org/10.1016/j.jocs.2020.101168. HAL: https://hal.science/hal-02919422.

2. N. Melab, A. Y. Zomaya and I. Chakroun. Parallel optimization using/for multi and many-core high performance computing. Journal of Parallel and Distributed Computing, Elsevier, Vol. 112, pages 109-110, 2018. DOI: https://doi.org/10.1016/j.jpdc.2017.11.011. HAL: https://hal.science/hal-01924680.

3. P. Korošec, N. Melab and E.-G. Talbi, editors. Bioinspired Optimization Methods and Their Applications - 8th International Conference, BIOMA 2018, Paris, France, May 16-18, 2018, Proceedings. Lecture Notes in Computer Science, Springer, Vol. 10835, pages XIII-333, 2018. DOI: https://doi.org/10.1007/978-3-319-91641-5. HAL: https://hal.science/hal-01942377.

4. N. Melab and M. Mezmaz. Multi and many-core computing for parallel metaheuristics. Concurrency and Computation: Practice and Experience, Wiley, Vol. 29(9), pages 1-5, 2017. DOI: https://doi.org/10.1002/cpe.4116. HAL: https://hal.science/hal-01648278.

Journal articles

1. J.-P. Valois, G. Helbecque and N. Melab. Efficient and scalable branch-and-bound algorithm for exact qubit allocation. Future Generation Computer Systems, Elsevier, Vol. 179, article 108342, 2026. DOI: https://doi.org/10.1016/j.future.2025.108342. HAL: https://hal.science/hal-05449040.

2. G. Helbecque, E. Krishnasamy, T. Carneiro, N. Melab and P. Bouvry. Portable PGAS-based GPU-accelerated Branch-and-Bound Algorithms at Scale. Concurrency and Computation: Practice and Experience, Wiley, Vol. 37(25-26), article e70321, 2025. DOI: https://doi.org/10.1002/cpe.70321. HAL: https://hal.science/hal-05267434.

3. J. Rouzé, N. Melab, J. Gmys and D. Tuyttens. A parallel memetic algorithm for qubit mapping on noisy intermediate-scale quantum machines. Engineering Applications of Artificial Intelligence, Elsevier, Vol. 161, article 112081, 2025. DOI: https://doi.org/10.1016/j.engappai.2025.112081.

4. R. Ragonnet, A. E. Hughes, D. S. Shipman, M. T. Meehan, A. S. Henderson, G. Briffoteaux, N. Melab, D. Tuyttens, E. S. McBryde and J. M. Trauer. Estimating the impact of school closures on the COVID-19 dynamics in 74 countries: A modelling analysis. PLOS Medicine, Public Library of Science, Vol. 22(1), article e1004512, 2025. DOI: https://doi.org/10.1371/journal.pmed.1004512. HAL: https://hal.science/hal-05457913.

5. G. Briffoteaux, N. Melab, M. Mezmaz and D. Tuyttens. Investigating surrogate-based hybrid acquisition processes. Application to Covid-19 contact mitigation. Applied Soft Computing, Elsevier, Vol. 151, article 111134, 2024. DOI: https://doi.org/10.1016/j.asoc.2023.111134.

6. M. Gobert, G. Briffoteaux, J. Gmys, N. Melab and D. Tuyttens. Observations in applying Bayesian versus evolutionary approaches and their hybrids in parallel time-constrained optimization. Engineering Applications of Artificial Intelligence, Elsevier, Vol. 137, article 109075, 2024. DOI: https://doi.org/10.1016/j.engappai.2024.109075.

7. G. Helbecque, J. Gmys, N. Melab, T. Carneiro and P. Bouvry. Parallel distributed productivity-aware tree-search using Chapel. Concurrency and Computation: Practice and Experience, Wiley, Vol. 35(27), article e7874, 2023. DOI: https://doi.org/10.1002/cpe.7874. HAL: https://hal.science/hal-04165491.

8. J. Gamot, M. Balesdent, A. Tremolet, R. Wuilbercq, N. Melab and E.-G. Talbi. Hidden-variables genetic algorithm for variable-size design space optimal layout problems with application to aerospace vehicles. Engineering Applications of Artificial Intelligence, Elsevier, Vol. 121, article 105941, 2023. DOI: https://doi.org/10.1016/j.engappai.2023.105941.

9. M. Gobert, J. Gmys, J.-F. Toubeau, N. Melab, D. Tuyttens and F. Vallée. Batch Acquisition for Parallel Bayesian Optimization — Application to Hydro-Energy Storage Systems Scheduling. Algorithms, MDPI, Vol. 15(12), article 446, 2022. DOI: https://doi.org/10.3390/a15120446.

10. A. Hebbal, L. Brevault, M. Balesdent, N. Melab and E.-G. Talbi. Deep Gaussian process for multi-objective Bayesian optimization. Optimization and Engineering, Springer, 2022. DOI: https://doi.org/10.1007/s11081-022-09753-0. HAL: https://hal.science/hal-03770763.

11. A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi and N. Melab. Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes. Structural and Multidisciplinary Optimization, Springer, Vol. 63(5), pages 2267-2288, 2021. DOI: https://doi.org/10.1007/s00158-020-02802-1. HAL: https://hal.science/hal-03046270.

12. J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. A computationally efficient Branch-and-Bound algorithm for the permutation Flow-shop scheduling problem. European Journal of Operational Research, Elsevier, Vol. 284(3), pages 814-833, 2020. DOI: https://doi.org/10.1016/j.ejor.2020.01.039.

13. J. Gmys, T. Carneiro, N. Melab, E.-G. Talbi and D. Tuyttens. A comparative study of high-productivity high-performance programming languages for parallel metaheuristics. Swarm and Evolutionary Computation, Elsevier, Vol. 57, article 100720, 2020. DOI: https://doi.org/10.1016/j.swevo.2020.100720.

14. T. Carneiro, J. Gmys, N. Melab and D. Tuyttens. Towards ultra-scale Branch-and-Bound using a high-productivity language. Future Generation Computer Systems, Elsevier, Vol. 105, pages 196-209, 2020. DOI: https://doi.org/10.1016/j.future.2019.11.011.

15. G. Briffoteaux, R. Ragonnet, M. Mezmaz, N. Melab and D. Tuyttens. Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control. Future Generation Computer Systems, Elsevier, Vol. 113, pages 454-467, 2020. DOI: https://doi.org/10.1016/j.future.2020.07.005. HAL: https://hal.science/hal-02904840.

16. G. Briffoteaux, M. Gobert, R. Ragonnet, J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. Parallel surrogate-assisted optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO. Swarm and Evolutionary Computation, Elsevier, Vol. 57, article 100717, 2020. DOI: https://doi.org/10.1016/j.swevo.2020.100717. HAL: https://hal.science/hal-02767541.

17. T. Carneiro Pessoa, J. Gmys, F. Heron de Carvalho Junior, N. Melab and D. Tuyttens. GPU-accelerated backtracking using CUDA Dynamic Parallelism. Concurrency and Computation: Practice and Experience, Wiley, Vol. 30(9), 2018. DOI: https://doi.org/10.1002/cpe.4374.

18. N. Melab, J. Gmys, M. Mezmaz and D. Tuyttens. Multi-core versus many-core computing for many-task Branch-and-Bound applied to big optimization problems. Future Generation Computer Systems, Elsevier, Vol. 82, pages 472-481, 2018. DOI: https://doi.org/10.1016/j.future.2016.12.039. HAL: https://hal.science/hal-01419079.

19. E. Alekseeva, M. Mezmaz, D. Tuyttens and N. Melab. Parallel multi-core hyper-heuristic GRASP to solve permutation flow-shop problem. Concurrency and Computation: Practice and Experience, Wiley, Vol. 29(9), 2017. DOI: https://doi.org/10.1002/cpe.3835.

20. J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. IVM-based parallel branch-and-bound using hierarchical work stealing on multi-GPU systems. Concurrency and Computation: Practice and Experience, Wiley, Vol. 29(9), 2017. DOI: https://doi.org/10.1002/cpe.4019. HAL: https://hal.science/hal-01419072.

21. J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. IVM-based parallel branch-and-bound using hierarchical work stealing on multi-GPU systems. Concurrency and Computation: Practice and Experience, Wiley, Vol. 28(18), pages 4463-4484, 2016. DOI: https://doi.org/10.1002/cpe.4019. HAL: https://hal.science/hal-01419072.

22. J. Gmys, R. Leroy, M. Mezmaz, N. Melab and D. Tuyttens. Work Stealing with Private Integer-Vector-Matrix Data Structure for Multi-core Branch-and-Bound Algorithms. Concurrency and Computation: Practice and Experience, Wiley, Vol. 28(18), pages 4461-4557, 2016. DOI: https://doi.org/10.1002/cpe.3771.

23. J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. A GPU-based Branch-and-Bound algorithm using Integer-Vector-Matrix data structure. Parallel Computing, Elsevier, Vol. 59, pages 119-139, 2016. DOI: https://doi.org/10.1016/j.parco.2016.01.008. HAL: https://hal.science/hal-01389471.

24. I. Chakroun and N. Melab. Towards an heterogeneous and adaptive parallel Branch-and-Bound algorithm. Journal of Computer and System Sciences, Elsevier, Vol. 81(1), pages 72-84, 2015. HAL: https://hal.science/hal-00935899.

25. A. Bendjoudi, N. Melab and E.-G. Talbi. FTH-B&B: a Fault Tolerant Hierarchical Branch and Bound for Large Scale Unreliable Environments. IEEE Transactions on Computers, IEEE, Vol. 63(9), pages 2302-2315, 2014. HAL: https://hal.science/hal-00846013.

26. N. Melab, I. Chakroun and A. Bendjoudi. Graphics processing unit-accelerated bounding for branch-and-bound applied to a permutation problem using data access optimization. Concurrency and Computation: Practice and Experience, Wiley, Vol. 26(16), pages 2667-2683, 2014. DOI: https://doi.org/10.1002/cpe.3155. HAL: https://hal.science/hal-01095244.

27. M. Mezmaz, M. Mehdi, P. Bouvry, N. Melab, E.-G. Talbi and D. Tuyttens. Solving the three dimensional quadratic assignment problem on a computational grid. Cluster Computing, Springer, Vol. 17(2), pages 205-217, 2014. DOI: https://doi.org/10.1007/s10586-013-0313-4. HAL: https://hal.science/hal-01095464.

28. Y. Kessaci, N. Melab and E.-G. Talbi. A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager. Future Generation Computer Systems, Elsevier, Vol. 36, pages 237-256, 2014. DOI: https://doi.org/10.1016/j.future.2013.07.007. HAL: https://hal.science/hal-00924858.

29. Y. Kessaci, N. Melab and E.-G. Talbi. A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation. Cluster Computing, Springer, Vol. 16(3), pages 451-468, 2013. DOI: https://doi.org/10.1007/s10586-012-0210-2. HAL: https://hal.science/hal-00749048.

30. I. Chakroun, M. Mezmaz, N. Melab and A. Bendjoudi. Reducing thread divergence in a GPU-accelerated branch-and-bound algorithm. Concurrency and Computation: Practice and Experience, Wiley, Vol. 25(8), pages 1121-1136, 2013. DOI: https://doi.org/10.1002/cpe.2931. HAL: https://hal.science/hal-00731859.

31. I. Chakroun, N. Melab, M.-S. Mezmaz and D. Tuyttens. Combining multi-core and GPU computing for solving combinatorial optimization problems. Journal of Parallel and Distributed Computing, Elsevier, Vol. 73(12), pages 1563-1577, 2013. DOI: https://doi.org/10.1016/j.jpdc.2013.07.023. HAL: https://hal.science/hal-00935890.

32. T.-V. Luong, N. Melab and E.-G. Talbi. GPU Computing for Parallel Local Search Metaheuristic Algorithms. IEEE Transactions on Computers, IEEE, Vol. 62(1), pages 173-185, 2013. DOI: https://doi.org/10.1109/TC.2011.206.

33. A. Bendjoudi, N. Melab and E.-G. Talbi. Hierarchical branch and bound algorithm for computational grids. Future Generation Computer Systems, Elsevier, Vol. 28(8), pages 1168-1176, 2012. DOI: https://doi.org/10.1016/j.future.2012.03.001.

34. L. Loukil, M. Mehdi, N. Melab, E.-G. Talbi and P. Bouvry. Parallel Hybrid Genetic Algorithms for Solving Q3AP on Computational Grid. International Journal of Foundations of Computer Science, World Scientific, Vol. 23(2), pages 483-500, 2012. DOI: https://doi.org/10.1142/S0129054112400242. HAL: https://hal.science/hal-00750703.

35. A. Bendjoudi, N. Melab and E.-G. Talbi. An adaptive hierarchical master-worker (AHMW) framework for grids - Application to B&B algorithms. Journal of Parallel and Distributed Computing, Elsevier, Vol. 72(2), pages 120-131, 2012. DOI: https://doi.org/10.1016/j.jpdc.2011.10.002.

36. M. Mezmaz, N. Melab, Y. Kessaci, Y. C. Lee, E.-G. Talbi, A. Y. Zomaya and D. Tuyttens. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal of Parallel and Distributed Computing, Elsevier, Vol. 71(11), pages 1497-1508, 2011. DOI: https://doi.org/10.1016/j.jpdc.2011.04.007. HAL: https://hal.science/hal-00639966.

37. T.-V. Luong, N. Melab and E.-G. Talbi. Neighborhood Structures for GPU-based Local Search Algorithms. Parallel Processing Letters, World Scientific, Vol. 20(4), pages 307-324, 2010. DOI: https://doi.org/10.1142/S0129626410000260.

38. M. Mehdi, M. Mezmaz, N. Melab, E.-G. Talbi and P. Bouvry. P2P Computing for Large Tree Exploration-based Exact Optimisation. International Journal of Grid and Utility Computing, Inderscience, Vol. 1(3), pages 252-260, 2009. DOI: https://doi.org/10.1504/IJGUC.2009.027652.

39. A. Bendjoudi, N. Melab and E.-G. Talbi. P2P Design and Implementation of a Parallel Branch and Bound Algorithm for Grids. International Journal of Grid and Utility Computing, Inderscience, Vol. 1(2), pages 159-168, 2009. DOI: https://doi.org/10.1504/IJGUC.2009.022031.

40. A.-A. Tantar, N. Melab and E.-G. Talbi. A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Computing, Springer, Vol. 12(12), pages 1185-1198, 2008. DOI: https://doi.org/10.1007/s00500-008-0298-8. HAL: https://hal.science/hal-00688680.

41. A.-A. Tantar, S. Conilleau, B. Parent, N. Melab, L. Brillet, S. Roy, E.-G. Talbi and D. Horvath. Docking and Biomolecular Simulations on Computer Grids: Status and Trends. Current Computer-Aided Drug Design, Bentham Science Publishers, Vol. 4(3), pages 235-249, 2008. DOI: https://doi.org/10.2174/157340908785747438.

42. J.-M. Pierson, J. Gossa, P. Wherle, Y. Cardenas, S. Cahon, M. E. Samad, L. Brunie, C. Dhaenens, A. Hameurlain, N. Melab, M. Miquel, F. Morvan, E.-G. Talbi and A. Tchounikine. GGM Efficient Navigation and Mining in Distributed Geno-Medical Data. IEEE Transactions on Nanobioscience, IEEE, Vol. 6(2), pages 110-116, 2007. DOI: https://doi.org/10.1109/TNB.2007.897477. HAL: https://hal.science/hal-01515381.

43. E.-G. Talbi, S. Cahon and N. Melab. Designing cellular networks using a parallel hybrid metaheuristic. Computer Communications, Elsevier, Vol. 30(4), pages 698-713, 2007. DOI: https://doi.org/10.1016/j.comcom.2006.08.017.

44. A.-A. Tantar, N. Melab, E.-G. Talbi, O. Dragos and B. Parent. A Parallel Hybrid Genetic Algorithm for Protein Structure Prediction on the Computational Grid. Future Generation Computer Systems, Elsevier, Vol. 23(3), pages 398-409, 2007. DOI: https://doi.org/10.1016/j.future.2006.09.001. HAL: https://hal.science/hal-00684579.

45. M. Mezmaz, N. Melab and E.-G. Talbi. An Efficient Load Balancing Strategy for Grid-based Branch and Bound Algorithm. Parallel Computing, Elsevier, Vol. 33(4-5), pages 302-313, 2007. DOI: https://doi.org/10.1016/j.parco.2007.02.004. HAL: https://hal.science/hal-00684607.

46. M. Mezmaz, N. Melab and E.-G. Talbi. Combining Metaheuristics and Exact Methods for Solving Exactly Multi-Objective Problems on the Grid. Journal of Mathematical Modelling and Algorithms, Springer, Vol. 6(3), pages 393-409, 2007. DOI: https://doi.org/10.1007/s10852-007-9063-8.

47. N. Melab, M. Mezmaz and E.-G. Talbi. Parallel Cooperative Meta-heuristics on the Computational Grid. A Case Study: The Bi-objective Flow-Shop Problem. Parallel Computing, Elsevier, Vol. 32(9), pages 643-659, 2006. DOI: https://doi.org/10.1016/j.parco.2006.01.003. HAL: https://hal.science/hal-00690375.

48. R. Bolze, F. Cappello, E. Caron, M. Daydé, F. Desprez, E. Jeannot, Y. Jégou, S. Lanteri, J. Leduc, N. Melab, G. Mornet, R. Namyst, P. Primet, B. Quetier, O. Richard, E.-G. Talbi and T. Irena. Grid'5000: a large scale and highly reconfigurable experimental Grid testbed. International Journal of High Performance Computing Applications, SAGE, Vol. 20(4), pages 481-494, 2006. DOI: https://doi.org/10.1177/1094342006070078. HAL: https://hal.science/hal-00684943.

49. N. Melab, S. Cahon and E.-G. Talbi. Grid computing for parallel bioinspired algorithms. Journal of Parallel and Distributed Computing, Elsevier, Vol. 66(8), pages 1052-1061, 2006. DOI: https://doi.org/10.1016/j.jpdc.2005.11.006.

50. S. Cahon, N. Melab and E.-G. Talbi. ParadisEO: a Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, Kluwer Academic Publishers, Vol. 10(3), pages 357-380, 2004. DOI: https://doi.org/10.1023/B:HEUR.0000026900.92269.ec.

51. S. Cahon, N. Melab and E.-G. Talbi. Building with ParadisEO Reusable Parallel and Distributed Evolutionary Algorithms. Parallel Computing, Elsevier, Vol. 30(5-6), pages 677-697, 2004. DOI: https://doi.org/10.1016/j.parco.2003.12.010.

52. N. Melab, E.-G. Talbi and S. Petiton. A Parallel Adaptive Gauss-Jordan Algorithm. The Journal of Supercomputing, Kluwer Academic Publishers, Vol. 17(2), pages 167-185, 2001. DOI: https://doi.org/10.1023/A:1008182404262. HAL: https://hal.science/hal-01272404.

53. N. Melab. Data Mining: a Key Contribution to E-Business. Information & Communications Technology Law, Taylor & Francis, Vol. 10(3), pages 309-318, 2001. DOI: https://doi.org/10.1080/13600830120081935.

54. N. Melab and E.-G. Talbi. Parallel adaptive computing on meta-systems including NOWs. Parallel Computing, Elsevier, Vol. 26(2-3), pages 267-284, 2000. DOI: https://doi.org/10.1016/S0167-8191(99)00105-2.

55. L. Deruelle, M. Bouneffa, N. Melab and H. Basson. An expert system-based change propagation process for web sites maintenance. ISI-NIS, Hermes, Vol. 6(1), pages 123-148, 2001. DBLP: https://dblp.org/rec/journals/isi/DeruelleBMB01.

Conference articles

1. J.-P. Valois, T. Firmin and N. Melab. A Parallel Island Genetic Algorithm for Triangle-based Image Reconstruction. ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2025, IEEE, pages 1-8, 2025. DOI: https://doi.org/10.1109/AICCSA66935.2025.11315373. HAL: https://hal.science/hal-05458101.

2. I. Tagliaferro, G. Helbecque, E. Krishnasamy, N. Melab and G. Danoy. A Portable Branch-and-Bound Algorithm for Cross-Architecture Multi-GPU Systems. 23rd International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms, HeteroPar 2025, Dresden, Germany, 2025. HAL: https://hal.science/hal-05454431.

3. I. Tagliaferro, G. Helbecque, E. Krishnasamy, N. Melab and G. Danoy. Performance and Portability in Multi-GPU Branch-and-Bound: Chapel Versus CUDA and HIP for Tree-Based Optimization. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2025, IEEE, pages 1293-1295, 2025. DOI: https://doi.org/10.1109/IPDPSW66978.2025.00217. HAL: https://hal.science/hal-05263968.

4. T. Carneiro, E. Kayraklioglu, G. Helbecque and N. Melab. Investigating Portability in Chapel for Tree-Based Optimization on GPU-Powered Clusters. Euro-Par 2024: Parallel Processing, Springer, LNCS, Part III, pages 386-399, 2024. DOI: https://doi.org/10.1007/978-3-031-69583-4_27. HAL: https://hal.science/hal-04709120.

5. G. Helbecque, E. Krishnasamy, T. Carneiro, N. Melab and P. Bouvry. A Chapel-Based Multi-GPU Branch-and-Bound Algorithm: Application to the Flowshop Scheduling Problem. Euro-Par 2024 Workshops, HeteroPar 2024, Springer, pages 463-474, 2024. DOI: https://doi.org/10.1007/978-3-031-90200-0_37. HAL: https://hal.science/hal-04709106.

6. G. Helbecque, T. Carneiro, N. Melab, J. Gmys and P. Bouvry. PGAS Data Structure for Unbalanced Tree-Based Algorithms at Scale. International Conference on Computational Science, ICCS 2024, Springer, LNCS, pages 103-111, 2024. DOI: https://doi.org/10.1007/978-3-031-63759-9_13. HAL: https://hal.science/hal-04636184.

7. G. Helbecque, E. Krishnasamy, N. Melab and P. Bouvry. GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2024, IEEE, pages 872-879, 2024. DOI: https://doi.org/10.1109/IPDPSW63119.2024.00156. HAL: https://hal.science/hal-04551856.

8. G. Helbecque, E. Krishnasamy, N. Melab and P. Bouvry. GPU Computing in Chapel: Application to Tree-Search Algorithms. International Conference on Optimization and Learning, OLA 2024, Dubrovnik, Croatia, 2024. HAL: https://hal.science/hal-04551844.

9. J. Rouzé, N. Melab and D. Tuyttens. A Parallel Genetic Algorithm for Qubit Mapping on Noisy Intermediate-Scale Quantum Machines. International Conference on Optimization and Learning, OLA 2024, Springer, CCIS, pages 305-320, 2024. DOI: https://doi.org/10.1007/978-3-031-77941-1_24. HAL: https://hal.science/hal-04916922.

10. J. Gamot, M. Balesdent, R. Wuilbercq, A. Tremolet, N. Melab and E.-G. Talbi. Two-Level Algorithm Combining Bayesian Optimization and Swarm Intelligence for Variable-Size Optimal Layout Problems. Genetic and Evolutionary Computation Conference Companion, GECCO Companion 2023, ACM, pages 139-142, 2023. DOI: https://doi.org/10.1145/3583133.3596997. HAL: https://hal.science/hal-04381343.

11. G. Helbecque, J. Gmys, T. Carneiro, N. Melab and P. Bouvry. Towards a scalable load balancing for productivity-aware tree-search. 10th Annual Chapel Implementers and Users Workshop, CHIUW 2023, online, United States, 2023. HAL: https://hal.science/hal-04117159.

12. G. Helbecque, J. Gmys, N. Melab, T. Carneiro and P. Bouvry. Productivity-aware Parallel Distributed Tree-Search for Exact Optimization. International Conference on Optimization and Learning, OLA 2023, Malaga, Spain, 2023. HAL: https://hal.science/hal-04096195.

13. J. Gamot, R. Wuilbercq, M. Balesdent, A. Tremolet, N. Melab and E.-G. Talbi. Component Swarm Optimization Using Virtual Forces for Solving Layout Problems. International Conference on Swarm Intelligence, ANTS 2022, Springer, pages 292-299, 2022. DOI: https://doi.org/10.1007/978-3-031-20176-9_25. HAL: https://hal.science/hal-03772351.

14. G. Briffoteaux, N. Melab, M. Mezmaz and D. Tuyttens. Hybrid Acquisition Processes in Surrogate-Based Optimization. Application to Covid-19 Contact Reduction. International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2022, Springer, pages 127-141, 2022. DOI: https://doi.org/10.1007/978-3-031-21094-5_10. HAL: https://hal.science/hal-03770742.

15. N. Frohner, J. Gmys, N. Melab, G. R. Raidl and E.-G. Talbi. Parallel Beam Search for Combinatorial Optimization. International Conference on Parallel Processing Workshops, ICPP Workshops 2022, ACM, article 21, pages 1-8, 2022. DOI: https://doi.org/10.1145/3547276.3548633. HAL: https://hal.science/hal-03773423.

16. T. Carneiro, L. Koutsantonis, N. Melab, E. Kieffer and P. Bouvry. A Local Search for Automatic Parameterization of Distributed Tree Search Algorithms. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022, IEEE, pages 783-789, 2022. DOI: https://doi.org/10.1109/IPDPSW55747.2022.00132. HAL: https://hal.science/hal-03619760.

17. M. Gobert, J. Gmys, J.-F. Toubeau, N. Melab, D. Tuyttens and F. Vallée. Parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage Systems. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2022, IEEE, pages 790-797, 2022. DOI: https://doi.org/10.1109/IPDPSW55747.2022.00133. HAL: https://hal.science/hal-03701671.

18. G. Briffoteaux, R. Ragonnet, P. Tomenko, M. Mezmaz, N. Melab and D. Tuyttens. Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. International Conference on Optimization and Learning, OLA 2022, Springer, pages 201-212, 2022. DOI: https://doi.org/10.1007/978-3-031-22039-5_16. HAL: https://hal.science/hal-03689674.

19. G. Helbecque, J. Gmys, T. Carneiro, N. Melab and P. Bouvry. A performance-oriented comparative study of the Chapel high-productivity language to conventional programming environments. 13th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2022, ACM, pages 21-29, 2022. DOI: https://doi.org/10.1145/3528425.3529104. HAL: https://hal.science/hal-03629798.

20. N. Frohner, J. Gmys, N. Melab, G. R. Raidl and E.-G. Talbi. Parallel Beam Search for Combinatorial Optimization. 15th International Symposium on Combinatorial Search, SoCS 2022, pages 273-275, 2022. DOI: https://doi.org/10.1609/socs.v15i1.21783. HAL: https://hal.science/hal-03689638.

21. T. Carneiro and N. Melab. Towards Ultra-scale Exact Optimization Using Chapel. 8th Annual Chapel Implementers and Users Workshop, CHIUW 2021, Seattle, United States, 2021. HAL: https://hal.science/hal-03326294.

22. G. Briffoteaux, R. Ragonnet, M. Mezmaz, N. Melab and D. Tuyttens. Evolution Control Ensemble Models for Surrogate-Assisted Evolutionary Algorithms. International Conference on High Performance Computing & Simulation, HPCS 2020, Barcelona / Virtual, Spain, 2021. HAL: https://hal.science/hal-03332521.

23. T. Carneiro, N. Melab, A. Hayashi and V. Sarkar. Towards Chapel-based Exascale Tree Search Algorithms: dealing with multiple GPU accelerators. International Conference on High Performance Computing & Simulation, HPCS 2020, Barcelona / Virtual, Spain, 2021. HAL: https://hal.science/hal-03149394.

24. M. Gobert, J. Gmys, N. Melab and D. Tuyttens. Space Partitioning with multiple models for Parallel Bayesian Optimization. International Conference on Optimization and Learning, OLA 2021, Sicilia / Virtual, Italy, 2021. HAL: https://hal.science/hal-03324642.

25. M. Gobert, J. Gmys, N. Melab and D. Tuyttens. Adaptive Space Partitioning for Parallel Bayesian Optimization. International Conference on High Performance Computing & Simulation, HPCS 2020, Barcelona / Virtual, Spain, 2021. HAL: https://hal.science/hal-03121209.

26. G. Briffoteaux, R. Ragonnet, M. Mezmaz, N. Melab and D. Tuyttens. Towards Dynamic Selection of Evolution Controls in Parallel Bayesian Neural Network-assisted Genetic Algorithm. International Conference on Optimization and Learning, OLA 2020, Cadiz, Spain, 2020. HAL: https://hal.science/hal-02867819.

27. T. Carneiro and N. Melab. Productivity-Aware Design and Implementation of Distributed Tree-Based Search Algorithms. International Conference on Computational Science, ICCS 2019, Springer, LNCS 11536, pages 253-266, 2019. DOI: https://doi.org/10.1007/978-3-030-22734-0_19.

28. T. Carneiro and N. Melab. An Incremental Parallel PGAS-based Tree Search Algorithm. International Conference on High Performance Computing & Simulation, HPCS 2019, IEEE, pages 19-26, 2019. DOI: https://doi.org/10.1109/HPCS48598.2019.9188106.

29. A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi and N. Melab. Multi-fidelity modeling using DGPs: Improvements and a generalization to varying input space dimensions. Bayesian Deep Learning Workshop, BDL 2019, in conjunction with NeurIPS 2019, Vancouver, Canada, 2019.

30. M. Gobert, J. Gmys, J.-F. Toubeau, F. Vallée, N. Melab and D. Tuyttens. Surrogate-Assisted Optimization for Multi-stage Optimal Scheduling of Virtual Power Plants. International Conference on High Performance Computing & Simulation, HPCS 2019, IEEE, pages 113-120, 2019. DOI: https://doi.org/10.1109/HPCS48598.2019.9188065. HAL: https://hal.science/hal-02898960.

31. A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi and N. Melab. Multi-objective optimization using Deep Gaussian Processes: Application to Aerospace Vehicle Design. AIAA Scitech 2019 Forum, 2019. DOI: https://doi.org/10.2514/6.2019-1973.

32. A. Hebbal, L. Brevault, M. Balesdent, E.-G. Talbi and N. Melab. Efficient Global Optimization Using Deep Gaussian Processes. IEEE Congress on Evolutionary Computation, CEC 2018, IEEE, pages 1-8, 2018. DOI: https://doi.org/10.1109/CEC.2018.8477946.

33. G. Briffoteaux, N. Melab, M. Mezmaz and D. Tuyttens. An Adaptive Evolution Control based on Confident Regions for Surrogate-assisted Optimization. International Conference on High Performance Computing & Simulation, HPCS 2018, IEEE, pages 802-809, 2018. DOI: https://doi.org/10.1109/HPCS.2018.00130. HAL: https://hal.science/hal-01922708.

34. T. Carneiro, J. Gmys, N. Melab, F. H. de Carvalho Junior, P. P. Rebouças Filho and D. Tuyttens. Dynamic Configuration of CUDA Runtime Variables for CDP-Based Divide-and-Conquer Algorithms. International Meeting on High Performance Computing for Computational Science, VECPAR 2018, Springer, LNCS 11333, pages 16-30, 2018. DOI: https://doi.org/10.1007/978-3-030-15996-2_2. HAL: https://hal.science/hal-02178314.

35. T. C. Pessoa, J. Gmys, N. Melab, F. H. de Carvalho Junior and D. Tuyttens. A GPU-Based Backtracking Algorithm for Permutation Combinatorial Problems. International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2016, Springer, LNCS 10048, 2016.

36. I. Chakroun and N. Melab. HB&B@GRID: An heterogeneous grid-enabled Branch and Bound algorithm. International Conference on High Performance Computing & Simulation, HPCS 2016, IEEE, pages 697-704, 2016. DOI: https://doi.org/10.1109/HPCSim.2016.7568401.

37. N. Melab, R. Leroy, M. Mezmaz and D. Tuyttens. Parallel Branch-and-Bound using private IVM-based work stealing on Xeon Phi MIC coprocessor. International Conference on High Performance Computing & Simulation, HPCS 2015, IEEE, pages 394-399, 2015. HAL: https://hal.science/hal-01248335.

38. F. Legillon, N. Melab, D. Renard and E.-G. Talbi. A Multi-objective Evolutionary Algorithm for Cloud Platform Reconfiguration. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015, IEEE, pages 286-291, 2015.

39. J. Gmys, M. Mezmaz, N. Melab and D. Tuyttens. IVM-Based Work Stealing for Parallel Branch-and-Bound on GPU. International Conference on Parallel Processing and Applied Mathematics, PPAM 2015, Springer, pages 548-558, 2015. Best Paper Award.

40. M. Mezmaz, R. Leroy, N. Melab and D. Tuyttens. A Multi-Core Parallel Branch-and-Bound Algorithm Using Factorial Number System. IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014, IEEE, pages 1203-1212, 2014.

41. Y. Kessaci, N. Melab and E.-G. Talbi. Multi-level and Multi-objective Survey on Cloud Scheduling. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2014, IEEE, pages 480-488, 2014.

42. R. Leroy, M. Mezmaz, N. Melab and D. Tuyttens. Work Stealing Strategies For Multi-Core Parallel Branch-and-Bound Algorithm Using Factorial Number System. International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2014, ACM, 2014.

43. A. Bendjoudi, M. Chekini, M. Gharbi, M. Mehdi, K. Benatchba, F. Sitayeb-Benbouzid and N. Melab. Parallel B&B Algorithm for Hybrid Multi-core/GPU Architectures. International Conference on High Performance Computing and Communications, HPCC/EUC 2013, IEEE, pages 914-921, 2013.

44. Y. Kessaci, N. Melab and E.-G. Talbi. A Pareto-based genetic algorithm for optimized assignment of VM requests on a cloud brokering environment. IEEE Congress on Evolutionary Computation, CEC 2013, IEEE, pages 2496-2503, 2013.

45. F. Legillon, N. Melab, D. Renard and E.-G. Talbi. Cost minimization of service deployment in a multi-cloud environment. IEEE Congress on Evolutionary Computation, CEC 2013, IEEE, pages 2580-2587, 2013.

46. N. Melab, T.-V. Luong, K. Boufaras and E.-G. Talbi. ParadisEO-MO-GPU: a framework for parallel GPU-based local search metaheuristics. Genetic and Evolutionary Computation Conference, GECCO 2013, ACM, pages 1189-1196, 2013. Nominated for the Best Paper Award.

47. I. Chakroun and N. Melab. Operator-Level GPU-Accelerated Branch and Bound Algorithms. International Conference on Computational Science, ICCS 2013, Elsevier Procedia Computer Science, pages 280-289, 2013. DOI: https://doi.org/10.1016/j.procs.2013.05.190.

48. F. Legillon, N. Melab, D. Renard and E.-G. Talbi. Cost Minimization of Service Deployment in a Public Cloud Environment. IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW/NIDISC 2013, IEEE, pages 491-498, 2013.

49. N. Melab, I. Chakroun, M. Mezmaz and D. Tuyttens. A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem. IEEE International Conference on Cluster Computing, CLUSTER 2012, IEEE, pages 10-17, 2012. DOI: https://doi.org/10.1109/CLUSTER.2012.17. HAL: https://hal.science/hal-00723736.

50. T.-T. Vu, B. Derbel, A. Assim, A. Bendjoudi and N. Melab. Overlay-Centric Load Balancing: Applications to UTS and B&B. IEEE International Conference on Cluster Computing, CLUSTER 2012, IEEE, pages 382-390, 2012.

51. Y. Kessaci, N. Melab and E.-G. Talbi. An Energy-aware Multi-start Local Search Heuristic for Scheduling VMs on the OpenNebula Cloud Distribution. International Conference on High Performance Computing & Simulation, HPCS 2012, IEEE, pages 112-118, 2012.

52. I. Chakroun and N. Melab. An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem. International Conference on High Performance Computing and Communications, HPCC 2012, IEEE, pages 389-395, 2012. DOI: https://doi.org/10.1109/HPCSim.2012.6266899. arXiv: https://arxiv.org/abs/1206.4973.

53. T.-V. Luong, E.-D. Taillard, N. Melab and E.-G. Talbi. Parallelization Strategies for Hybrid Metaheuristics Using a Single GPU and Multi-core Resources. Parallel Problem Solving from Nature, PPSN 2012, Springer, LNCS, pages 368-377, 2012.

54. M. Mehdi, J.-C. Charr, N. Melab, E.-G. Talbi and P. Bouvry. A New Cooperative Hybrid Approach Combining Genetic Algorithms and Branch-and-Bound for Solving Permutation-based Problems. Genetic and Evolutionary Computation Conference, GECCO 2011, ACM, Dublin, Ireland, 2011. Best Paper Award.

55. Y. Kessaci, N. Melab and E.-G. Talbi. A Pareto GA for Green Scheduling of HPC Applications on Distributed Cloud. International Workshop on Optimization Issues in Grid and Parallel Computing, OPTIM/HPCS 2011, Istanbul, Turkey, 2011.

56. N. Melab, T.-V. Luong, K. Boufaras and E.-G. Talbi. Towards ParadisEO-MO-GPU: a Framework for GPU-based Local Search Metaheuristics. International Work-Conference on Artificial Neural Networks, IWANN 2011, Springer, LNCS 6691, Part I, 2011.

57. M. Djamaï, B. Derbel and N. Melab. Distributed B&B: A Pure Peer-to-Peer Approach. IEEE International Parallel and Distributed Processing Symposium Workshops, LSPP/IPDPS 2011, IEEE, Anchorage, Alaska, 2011.

58. A. Bendjoudi, N. Melab and E.-G. Talbi. Fault-Tolerant Mechanism for Hierarchical Branch and Bound Algorithm. IEEE International Parallel and Distributed Processing Symposium Workshops, LSPP/IPDPS 2011, IEEE, Anchorage, Alaska, 2011.

59. T.-V. Luong, N. Melab and E.-G. Talbi. GPU-based Approaches for Multiobjective Local Search Algorithms. A Case Study: the Flowshop Scheduling Problem. European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, Springer, LNCS, pages 155-166, 2011.

60. T.-V. Luong, N. Melab and E.-G. Talbi. GPU-based Multi-start Local Search Algorithms. Learning and Intelligent Optimization, LION 5, Springer, pages 321-335, 2011. DOI: https://doi.org/10.1007/978-3-642-25566-3_24.

61. T.-V. Luong, N. Melab and E.-G. Talbi. GPU-based Island Model for Evolutionary Algorithms. Genetic and Evolutionary Computation Conference, GECCO 2010, ACM, Portland, USA, 2010.

62. M. Mehdi, N. Melab, E.-G. Talbi and P. Bouvry. Interval-based Initialization Method for Permutation-based Problems. IEEE Congress on Evolutionary Computation, CEC 2010, IEEE, Barcelona, Spain, 2010.

63. M. Mezmaz, Y. C. Lee, N. Melab, E.-G. Talbi and A. Y. Zomaya. A Bi-objective Hybrid Genetic Algorithm to Minimize Energy Consumption and Makespan for Precedence-constrained Applications Using Dynamic Voltage Scaling. IEEE Congress on Evolutionary Computation, CEC 2010, IEEE, Barcelona, Spain, 2010.

64. T.-V. Luong, N. Melab and E.-G. Talbi. GPU-based Parallel Hybrid Evolutionary Algorithms. IEEE Congress on Evolutionary Computation, CEC 2010, IEEE, Barcelona, Spain, 2010.

65. T.-V. Luong, L. Loukil, N. Melab and E.-G. Talbi. A GPU-based Iterated Tabu Search for Solving the Quadratic 3-dimensional Assignment Problem. ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010, Workshop on Parallel Optimization in Emerging Computing Environments, POECE, Hammamet, Tunisia, 2010. HAL: https://hal.science/inria-00520468.

66. T.-V. Luong, N. Melab and E.-G. Talbi. Large Neighborhood Local Search Optimization on Graphics Processing Units. IEEE International Parallel & Distributed Processing Symposium Workshops, LSPP/IPDPS 2010, IEEE, Atlanta, USA, 2010.

67. T.-V. Luong, N. Melab and E.-G. Talbi. Local Search Algorithms on Graphics Processing Units. A Case Study: the Permutation Perceptron Problem. European Conference on Evolutionary Computation in Combinatorial Optimisation, EvoCOP 2010, Springer, LNCS, Istanbul, Turkey, 2010. Nominated for the Best Paper Award.

68. M. Mehdi, N. Melab, E.-G. Talbi and P. Bouvry. Interval Island Model Initialization for Permutation-based Problems. Genetic and Evolutionary Computation Conference, GECCO 2009, Poster, Montreal, Canada, 2009.

69. L. Loukil, M. Mehdi, N. Melab, E.-G. Talbi and P. Bouvry. A Parallel Hybrid Genetic Algorithm - Simulated Annealing for Solving Q3AP on Computational Grid. IEEE International Parallel and Distributed Processing Symposium Workshops, NIDISC/IPDPS 2009, IEEE, Rome, Italy, 2009.

70. J. Tavares, A.-A. Tantar, N. Melab and E.-G. Talbi. The Influence of Mutation on Protein-Ligand Docking Optimization: a Locality Analysis. Parallel Problem Solving from Nature, PPSN 2008, Springer, LNCS 5199, pages 589-598, 2008.

71. M. Mehdi, M. Mezmaz, N. Melab, E.-G. Talbi and P. Bouvry. An Efficient Hybrid P2P Approach for Non redundant Tree Exploration in B&B Algorithms. International Workshop on P2P, Parallel, Grid and Internet Computing, CISIS 2008, IEEE, pages 354-359, Barcelona, Spain, 2008.

72. J. Tavares, A.-A. Tantar, N. Melab and E.-G. Talbi. The Impact of Local Search on Protein-Ligand Docking Optimization. International Conference on Hybrid Intelligent Systems, HIS 2008, IEEE, pages 53-58, Barcelona, Spain, 2008.

73. B. Parent, A. Tantar, N. Melab, E.-G. Talbi and D. Horvath. Grid-based evolutionary strategies applied to the conformational sampling problem. IEEE Congress on Evolutionary Computation, CEC 2007, IEEE, pages 291-296, Singapore, 2007.

74. I. Zunino, N. Melab and E.-G. Talbi. A Grid-enabled Framework for Exact Optimization Algorithms. High Performance Computing and Simulation Conference, HPCS 2007, Prague, Czech Republic, 2007.

75. A. Bendjoudi, N. Melab and E.-G. Talbi. Parallel Branch-and-Bound Algorithm on a Peer-to-Peer System. IEEE International Workshop on Global and Peer-to-Peer Computing, in conjunction with CCGRID 2007, Rio de Janeiro, Brazil, 2007.

76. M. Mezmaz, N. Melab and E.-G. Talbi. A Grid-enabled Branch and Bound Algorithm for Solving Challenging Combinatorial Optimization Problems. IEEE International Parallel and Distributed Processing Symposium, IPDPS 2007, IEEE, Long Beach, California, 2007.

77. A.-A. Tantar, N. Melab and E.-G. Talbi. A Comparative Study of Parallel Metaheuristics for Protein Structure Prediction on the Computational Grid. IEEE International Workshop on Nature Inspired Distributed Computing, NIDISC/IPDPS 2007, Long Beach, California, 2007.

78. M. Mezmaz, N. Melab and E.-G. Talbi. A Grid-based Parallel Approach of the Multi-Objective Branch and Bound. Euromicro Conference on Parallel, Distributed and Network-based Processing, PDP 2007, IEEE, Naples, Italy, 2007.

79. M. Mehdi, M. Mezmaz, N. Melab and E.-G. Talbi. Toward a Napster-like P2P B&B Algorithm and Island Models for Parallel Multi-objective Optimization on the Computational Grid. High Performance Computing and Simulation Conference, HPCS 2007, Prague, Czech Republic, 2007.

80. M. Mezmaz, N. Melab and E.-G. Talbi. Using the Multi-Start and Island Models for Parallel Multiobjective Optimization on the Computational Grid. IEEE International Workshop on Biologically inspired Optimization Methods for Parallel and Distributed Architectures, in conjunction with e-Science and Grid Computing, Amsterdam, The Netherlands, 2006.

81. N. Melab, M. Mezmaz, E.-G. Talbi and B. Wei. Parallel Peer-to-Peer Multi-objective Optimization using the Multi-Start Model. IFIP International Conference on Network and Parallel Computing, NPC 2006, pages 20-26, Tokyo, Japan, 2006.

82. A. Tantar, N. Melab, E.-G. Talbi and B. Toursel. Solving the Protein Folding Problem with a Bicriterion Genetic Algorithm on the Grid. IEEE International Workshop on Biomedical Computations on the Grid, BioGrid/CCGRID 2006, Singapore, 2006.

83. S. Cahon, E.-G. Talbi and N. Melab. A Parallel and Hybrid Multi-Objective Evolutionary Algorithm applied to the Design of Cellular Networks. Mediterranean Electrotechnical Conference, MELECON 2006, Benalmádena, Spain, 2006.

84. M. Mezmaz, N. Melab and E.-G. Talbi. A Grid Exact Hybrid Approach for Solving Multi-Objective Problems. IEEE International Workshop on Nature Inspired Distributed Computing, NIDISC/IPDPS 2006, Rhodes Island, Greece, 2006.

85. S. Cahon, N. Melab and E.-G. Talbi. An Enabling Framework for Parallel Optimization on the Computational Grid. IEEE International Symposium on Cluster Computing and the Grid, CCGRID 2005, Cardiff, UK, 2005.

86. J.-M. Pierson, L. Brunie, C. Dhaenens, A. Hameurlain, N. Melab, M. Miquel, F. Morvan, E.-G. Talbi and A. Tchounikine. Grid for Geno-Medicine: A Glimpse on the GGM Project. IEEE International Workshop on Biomedical Computations on the Grid, BioGrid/CCGRID 2005, Cardiff, UK, 2005.

87. N. Melab, M. Mezmaz and E.-G. Talbi. Parallel Hybrid Multi-objective Island Model in Peer-to-Peer Environment. IEEE International Workshop on Nature Inspired Distributed Computing, NIDISC/IPDPS 2005, Denver, Colorado, 2005.

88. F. Cappello, F. Desprez, M. Daydé, E. Jeannot, Y. Jégou, S. Lanteri, N. Melab, R. Namyst, P. Primet, O. Richard, E. Caron, J. Leduc and G. Mornet. Grid'5000: a large scale, reconfigurable, controllable and monitorable Grid platform. IEEE/ACM International Workshop on Grid Computing, GRID 2005, Seattle, Washington, USA, 2005.

89. M. Mezmaz, N. Melab and E.-G. Talbi. Towards a Coordination Model for Parallel Cooperative P2P Multi-objective Optimization. European Grid Conference, EGC 2005, Springer, LNCS 3470, pages 305-314, Amsterdam, The Netherlands, 2005.

90. S. Cahon, N. Melab, E.-G. Talbi and M. Schoenauer. ParadisEO-based Design of Parallel and Distributed Evolutionary Algorithms. Evolutionary Algorithms, EA 2003, revised selected paper, Springer, LNCS 2936, pages 216-228, 2004.

91. L. Brunie, M. Miquel, J.-M. Pierson, A. Tchounikine, C. Dhaenens, N. Melab, E.-G. Talbi, A. Hameurlain and F. Morvan. Information grids: managing and mining semantic data in a grid infrastructure; open issues and application to geno-medical data. International Conference on Database and Expert Systems Applications, DEXA 2003, IEEE, pages 509-518, Prague, Czech Republic, 2003.

92. S. Cahon, E.-G. Talbi and N. Melab. ParaDisEO: a Framework for Parallel and Distributed Biologically Inspired Metaheuristics. IEEE International Workshop on Nature Inspired Distributed Computing, NIDISC/IPDPS 2003, Nice, France, 2003.

93. N. Melab, S. Cahon, E.-G. Talbi and L. Duponchel. Parallel GA-based Wrapper Feature Selection for Spectroscopic Data Mining. IEEE International Workshop on Bio-Inspired Solutions to Parallel Processing Problems, BioSP3 2002, Fort Lauderdale, Florida, USA, 2002.

94. N. Melab, L. Deruelle, M. Bouneffa and H. Basson. Towards a Changeability Assessment of Distributed Multi-language Software Code. IEEE International Workshop on Empirical Studies of Software Maintenance, WESS 2001, Florence, Italy, 2001.

95. L. Deruelle, M. Bouneffa, N. Melab and H. Basson. Analysis and Manipulation of Distributed MultiLanguage Software Code. IEEE International Workshop on Source Code Analysis and Manipulation, SCAM 2001, Florence, Italy, 2001.

96. L. Deruelle, M. Bouneffa, N. Melab and H. Basson. A Change Propagation Model and Platform for MultiDatabase Applications. IEEE International Conference on Software Maintenance, ICSM 2001, Florence, Italy, 2001.

97. L. Deruelle, N. Melab, M. Bouneffa and H. Basson. Web Site Evolution based on Change Impact Analysis. International Conference on Information Systems and Engineering, ISE 2001, Las Vegas, USA, 2001.

98. N. Melab and E.-G. Talbi. A Parallel Genetic Algorithm for Rule Mining. IEEE International Workshop on Bio-Inspired Solutions to Parallel Processing Problems, BioSP3 2001, San Francisco, USA, 2001.

99. N. Melab, M. Bouneffa, L. Deruelle and H. Basson. Change Propagation in Multi-language Distributed Software. International Conference on Parallel and Distributed Computing Systems, PDCS 2000, pages 472-477, Las Vegas, Nevada, USA, 2000.

100. N. Melab and L. Deruelle. Toward a Model and a Platform for Profiling of Multi-language Distributed Object Software. International Conference on Parallel and Distributed Computing Systems, PDCS 2000, pages 275-280, Las Vegas, Nevada, USA, 2000.

101. N. Melab, L. Deruelle, M. Bouneffa and H. Basson. Instrumentation-based Profiling Techniques. ISCA International Conference on Computers and Their Applications, CATA 2000, New Orleans, Louisiana, USA, 2000.

102. N. Melab, H. Basson, M. Bouneffa and L. Deruelle. Performance of Object-oriented Code: Profiling and Instrumentation. IEEE International Conference on Software Maintenance, ICSM 1999, Oxford, UK, 1999.

103. N. Melab, E.-G. Talbi and S. Petiton. A Parallel Adaptive version of the Block-based Gauss-Jordan Algorithm. IEEE International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing, IPPS/SPDP 1999, pages 350-355, San Juan, Puerto Rico, 1999.

104. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. Increasing the Granularity of Parallelism in the P3 Parallel Evaluation Scheme of Functional Languages. International Conference on Parallel and Distributed Computing Systems, PDCS 1997, pages 578-583, New Orleans, Louisiana, USA, 1997.

105. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. A Periodic Adaptive Strategy for Monitoring Distributed Applications: Description and Analysis. International Symposium on High Performance Computing Systems, HPCS 1997, pages 645-654, Winnipeg, Manitoba, Canada, 1997.

106. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. Adaptive Load Balancing and Multithreading. International Conference on Parallel and Distributed Computing Systems, PDCS 1996, pages 343-348, Vol. 1, Dijon, France, 1996. Best Paper Award from IEEE France.

107. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. Adaptive load balancing of irregular applications. A case study: IDA applied to the 15-puzzle problem*. International Workshop on Solving Irregularly Structured Problems in Parallel, IRREGULAR 1996, Springer, LNCS 1117, pages 327-338, Santa Barbara, California, USA, 1996.

108. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. An adaptive Load Information Collection Policy. International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 1996, pages 649-658, Sunnyvale, California, USA, 1996.

109. N. Melab, N. Devesa, M. P. Lecouffe and B. Toursel. An Adaptive Load Balancing Algorithm with a Multithreaded Implementation. International Conference on Systems Engineering, ICSE 1996, pages 97-102, Las Vegas, Nevada, USA, 1996.

Book chapters

1. N. Melab, J. Gmys, M. Mezmaz and D. Tuyttens. Many-Core Branch-and-Bound for GPU Accelerators and MIC Coprocessors. High-Performance Simulation-Based Optimization, Studies in Computational Intelligence, Springer, Vol. 833, pages 275-291, 2020. DOI: https://doi.org/10.1007/978-3-030-18764-4_12. HAL: https://hal.science/hal-01924766.

2. L. Brevault, J. Pelamatti, A. Hebbal, M. Balesdent, E.-G. Talbi and N. Melab. MDO Related Issues: Multi-Objective and Mixed Continuous/Discrete Optimization. Aerospace System Analysis and Optimization in Uncertainty, Springer Optimization and Its Applications, Springer, Vol. 156, pages 321-358, 2020. DOI: https://doi.org/10.1007/978-3-030-39126-3_9.

3. I. Chakroun and N. Melab. GPU-Accelerated Tree-Based Exact Optimization Methods. Designing Scientific Applications on GPUs, Chapman & Hall/CRC, Taylor & Francis Group, 2014. Éditeur: https://www.routledge.com/Designing-Scientific-Applications-on-GPUs/Couturier/p/book/9781032919263.

4. L. Loukil, M. Mehdi, A. Bendjoudi and N. Melab. Parallel GPU-Accelerated Metaheuristics. Designing Scientific Applications on GPUs, Chapman & Hall/CRC, Taylor & Francis Group, 2014. Éditeur: https://www.routledge.com/Designing-Scientific-Applications-on-GPUs/Couturier/p/book/9781032919263.

5. M. Mezmaz, N. Melab and D. Tuyttens. A Multithreaded Branch-and-Bound Algorithm for Solving the Flow-Shop Problem on a Multicore Environment. Large Scale Network-Centric Distributed Systems, Wiley, Chapter 3, pages 53-70, 2013. DOI: https://doi.org/10.1002/9781118640708.ch03.

6. M. Djamaï, B. Derbel and N. Melab. Large-Scale P2P-Inspired Problem-Solving: A Formal and Experimental Study. Large Scale Network-Centric Distributed Systems, Wiley, Chapter 4, pages 71-102, 2013. DOI: https://doi.org/10.1002/9781118640708.ch04.

7. Y. Kessaci, M. Mezmaz, N. Melab and E.-G. Talbi. Parallel Evolutionary Algorithms for Energy Aware Scheduling. Intelligent Decision Systems in Large-Scale Distributed Environments, Studies in Computational Intelligence, Springer, Vol. 362, pages 75-100, 2011. DOI: https://doi.org/10.1007/978-3-642-21271-0_4.

8. A.-A. Tantar, N. Melab and E.-G. Talbi. A Grid-Based Hybrid Hierarchical Genetic Algorithm for Protein Structure Prediction. Parallel and Distributed Computational Intelligence, Studies in Computational Intelligence, Springer, pages 291-319, 2010. DOI: https://doi.org/10.1007/978-3-642-10675-0_13. DBLP: https://dblp.org/rec/series/sci/TantarMT10.

9. A.-A. Tantar, N. Melab and E.-G. Talbi. An Analysis of Dynamic Mutation Operators for Conformational Sampling. Biologically-Inspired Optimisation Methods, Studies in Computational Intelligence, Springer, Vol. 210, pages 291-323, 2009. DOI: https://doi.org/10.1007/978-3-642-01262-4_11.

10. E. Tantar, A.-A. Tantar, N. Melab and E.-G. Talbi. Landscape Analysis in Adaptive Metaheuristics for Grid Computing. Parallel Programming, Models and Applications in Grid and P2P Systems, Advances in Parallel Computing, IOS Press, pages 313-344, 2009. DOI: https://doi.org/10.3233/978-1-60750-004-9-313. DBLP: https://dblp.org/rec/series/apc/TantarTMT09.

11. A.-A. Tantar, N. Melab and E.-G. Talbi. Molecular Docking Using Grid Computing. Grid Computing for Bioinformatics and Computational Biology, Wiley Series in Bioinformatics, John Wiley & Sons, Chapter 8, pages 179-198, 2007. DOI: https://doi.org/10.1002/9780470191637.ch8.

12. A. Bendjoudi, S. Guerdah, M. Mansoura, N. Melab and E.-G. Talbi. P2P B&B and GA for the Flow-Shop Scheduling Problem. Metaheuristics for Scheduling in Distributed Computing Environments, Studies in Computational Intelligence, Springer, Vol. 146, pages 301-321, 2008. DOI: https://doi.org/10.1007/978-3-540-69277-5_11. HAL: https://hal.science/hal-00690360.

13. N. Melab, E.-G. Talbi and S. Cahon. On Parallel Evolutionary Algorithms on the Computational Grid. Parallel Evolutionary Computations, Studies in Computational Intelligence, Springer, Vol. 22, pages 117-132, 2006. DOI: https://doi.org/10.1007/3-540-32839-4_6.

14. N. Melab, E.-G. Talbi, S. Cahon, E. Alba and G. Luque. Parallel Metaheuristics: Algorithms and Frameworks. Parallel Combinatorial Optimization, Wiley Series on Parallel and Distributed Computing, John Wiley & Sons, Chapter 6, pages 149-161, 2006. DOI: https://doi.org/10.1002/9780470053928.ch6.

15. C. Dhaenens, J. Lemesre, N. Melab, M. Mezmaz and E.-G. Talbi. Parallel Exact Methods for Multiobjective Combinatorial Optimization. Parallel Combinatorial Optimization, Wiley Series on Parallel and Distributed Computing, John Wiley & Sons, Chapter 8, pages 187-210, 2006. DOI: https://doi.org/10.1002/9780470053928.ch8.

16. N. Melab, E.-G. Talbi and S. Cahon. Frameworks for the Design of Reusable Parallel and Distributed Metaheuristics. Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, Taylor & Francis, Chapter 35, pages 639-647, 2006. DOI: https://doi.org/10.1201/9781420035063.ch35.

17. N. Melab, E.-G. Talbi, M. Mezmaz and B. Wei. Parallel Hybrid Multi-objective Meta-heuristics on P2P Systems. Handbook of Bioinspired Algorithms and Applications, Chapman & Hall/CRC, Taylor & Francis, Chapter 36, pages 649-663, 2006. DOI: https://doi.org/10.1201/9781420035063.ch36.

18. S. Cahon, N. Melab et E.-G. Talbi. Sélection d’attributs en fouille de données sur grilles. Extraction des connaissances : État et perspectives, RNTI E-5, Cépaduès-Éditions, Chapter 6, pages 203-208, 2005. URL: https://editions-rnti.fr/?inprocid=1001498.

19. E.-G. Talbi, E. Alba, N. Melab and G. Luque. Metaheuristics and Parallelism. Parallel Metaheuristics: A New Class of Algorithms, Wiley Series on Parallel and Distributed Computing, John Wiley & Sons, Chapter 4, pages 79-104, 2005. DOI: https://doi.org/10.1002/0471739383.ch4.

20. L. Deruelle, M. Bouneffa, N. Melab and H. Basson. An Expert System-Based Change Propagation Process for Web Sites Maintenance. ISI-NIS, Hermes, Vol. 6(1), pages 123-148, 2001. DBLP: https://dblp.org/rec/journals/isi/DeruelleBMB01.