Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biology
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Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biologyAutor(es)
Data
2019-05-08Cita bibliográfica
González, P., Argüeso-Alejandro, P., Penas, D.R. et al. Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biology. J Supercomput 75, 3471–3498 (2019). https://doi.org/10.1007/s11227-019-02871-0
Resumo
[Abstract]
This paper describes and assesses a parallel multimethod hyperheuristic for the solution of complex global optimization problems. In a multimethod hyperheuristic, different metaheuristics cooperate to outperform the results obtained by any of them isolated. The results obtained show that the cooperation of individual parallel searches modifies the systemic properties of the hyperheuristic, achieving significant performance improvements versus the sequential and the non-cooperative parallel solutions. Here we present and evaluate a hybrid parallel scheme of the multimethod, using both message-passing (MPI) and shared memory (OpenMP) models. The hybrid parallelization allows to achieve a better trade-off between performance and computational resources, through a compromise between diversity (number of islands) and intensity (number of threads per island). For the performance evaluation, we considered the general problem of reverse engineering nonlinear dynamic models in systems biology, which yields very large mixed-integer dynamic optimization problems. In particular, three very challenging problems from the domain of dynamic modeling of cell signaling were used as case studies. In addition, experiments have been carried out in a local cluster, a large supercomputer and a public cloud, to show the suitability of the proposed solution in different execution platforms.
Palabras chave
Reverse engineering
Computational systems biology
Mixed-integer optimization problems
Parallel metaheuristics
Global optimization
Multimethod optimization
Computational systems biology
Mixed-integer optimization problems
Parallel metaheuristics
Global optimization
Multimethod optimization
Versión do editor
Dereitos
This is a post-peer-review, pre-copyedit version of an article published in Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-019-02871-0
ISSN
1573-0484
0920-8542
0920-8542