The MALLBA project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C++. MALLBA offers three families of generic resolution methods: exact, heuristic and hybrid. Moreover, for each resolution method, MALLBA provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks (currently under development). This paper explains the architecture of the MALLBA library, presents some of its skeletons, and offers several computational results to show the viability of the approach.

References in zbMATH (referenced in 28 articles , 1 standard article )

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  1. Xue, Ning; Bai, Ruibin; Qu, Rong; Aickelin, Uwe: A hybrid pricing and cutting approach for the multi-shift full truckload vehicle routing problem (2021)
  2. Herrera, Juan F. R.; Salmerón, José M. G.; Hendrix, Eligius M. T.; Asenjo, Rafael; Casado, Leocadio G.: On parallel branch and bound frameworks for global optimization (2017)
  3. Mason, Luke R.; Mak-Hau, Vicky H.; Ernst, Andreas T.: A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning (2015)
  4. Apolloni, Javier; García-Nieto, José; Alba, Enrique; Leguizamón, Guillermo: Empirical evaluation of distributed differential evolution on standard benchmarks (2014)
  5. Alba, Enrique; Luque, Gabriel; Nesmachnow, Sergio: Parallel metaheuristics: recent advances and new trends (2013)
  6. Humeau, J.; Liefooghe, A.; Talbi, E.-G.; Verel, S.: ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms (2013)
  7. Parejo, José Antonio; Ruiz-Cortés, Antonio; Lozano, Sebastián; Fernandez, Pablo: Metaheuristic optimization frameworks: a survey and benchmarking (2012) ioport
  8. García-Nieto, José; Alba, Enrique: Restart particle swarm optimization with velocity modulation: a scalability test (2011) ioport
  9. Levin, Mark Sh.: Four-layer framework for combinatorial optimization problems domain (2011) ioport
  10. Nesmachnow, Sergio; Cancela, Héctor; Alba, Enrique: Heterogeneous computing scheduling with evolutionary algorithms (2011) ioport
  11. Bożejko, Wojciech: A new class of parallel scheduling algorithms. (2010)
  12. Jourdan, L.; Basseur, M.; Talbi, E.-G.: Hybridizing exact methods and metaheuristics: a taxonomy (2009)
  13. Alba, Enrique; Dorronsoro, Bernabé: Cellular genetic algorithms (2008)
  14. Chicano, Francisco; Alba, Enrique: Ant colony optimization with partial order reduction for discovering safety property violations in concurrent models (2008)
  15. León, C.; Martín, S.; Miranda, G.; Rodríguez, C.; Rodríguez, J.: Parallelizations of the error correcting code problem (2008)
  16. Raidl, Günther R.; Puchinger, Jakob: Combining (integer) linear programming techniques and metaheuristics for combinatorial optimization (2008)
  17. Baravykaitė, M.; Čiegis, R.: An implementation of a parallel generalized branch and bound template (2007)
  18. Melab, N.; Cahon, S.; Talbi, E-G.: Grid computing for parallel bioinspired algorithms (2006)
  19. Salto, Carolina; Alba, Enrique; Molina, Juan M.: Analysis of distributed genetic algorithms for solving cutting problems (2006)
  20. Alba, E.; Talbi, E-G.; Luque, G.; Melab, N.: Metaheuristics and parallelism (2005)

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