Get Hybrid Metaheuristics: 6th International Workshop, HM 2009, PDF

By Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena (auth.), María J. Blesa, Christian Blum, Luca Di Gaspero, Andrea Roli, Michael Sampels, Andrea Schaerf (eds.)

ISBN-10: 3642049176

ISBN-13: 9783642049170

ISBN-10: 3642049184

ISBN-13: 9783642049187

This booklet constitutes the refereed complaints of the sixth overseas Workshop on Hybrid Metaheuristics, HM 2009, held in Udine, Italy, in October 2009.

The 12 revised complete papers offered including one invited speak have been rigorously reviewed and chosen from 22 submissions. The papers speak about present problems with combos of metaheuristics and different fixing ideas of common trouble reminiscent of novel combos of elements from various metaheuristics, hybridization of metaheuristics and AI/OR recommendations, low-level hybridization, high-level hybridization, portfolio concepts, specialist platforms, cooperative seek, computerized parameter tuning, empirical and statistical comparability, theoretical features of hybridization, parallelization, and software program libraries.

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Extra resources for Hybrid Metaheuristics: 6th International Workshop, HM 2009, Udine, Italy, October 16-17, 2009. Proceedings

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In this work, we further assume a homogeneous vehicle fleet with Q1 , . . , Qm = Q and D1 , . . , Dm = D. This article introduces a multiple variable neighborhood search (VNS) variant, where several cooperative VNS instances are running in an intertwined way. To obtain even better results, we further consider an integer linear programming (ILP) approach based on a set covering formulation and a column generation algorithm and hybridize it in a collaborative way with the multiple VNS. We refer to related work in Section 2.

8 Conclusion In this study we have shown that a hybrid algorithm resulting from the combination of Beam-ACO with constraint programming is an effective algorithm for the SMJS problem providing the best known results when optimizing makespan. This method uses CP effectively parallelizing the ACO solution construction and exploiting dependencies between partial solutions to find high quality solutions. There is certainly room to gain a better understanding of CP-Beam-MMAS’s performance. Moreover, more efficient uses of CP within this context may be explored, potentially leading to more time-efficient algorithms.

Lopez-Ibanez et al. (see [12]) have analysed stochastic sampling but the success of the method still warrants better understanding. Finally, CP-BeamMMAS has the potential to be extended to other combinatorial optimization problem types with non-trivial hard constraints where finding feasible solutions is not easy. Acknowledgements This work was supported by grant TIN2007-66523 (FORMALISM) of the Spanish government. Moreover, Christian Blum acknowledges support from the Ram´ on y Cajal program of the Spanish Ministry of Science and Innovation.

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Hybrid Metaheuristics: 6th International Workshop, HM 2009, Udine, Italy, October 16-17, 2009. Proceedings by Antonio Augusto Chaves, Luiz Antonio Nogueira Lorena (auth.), María J. Blesa, Christian Blum, Luca Di Gaspero, Andrea Roli, Michael Sampels, Andrea Schaerf (eds.)


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