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The Journal of Supercomputing

, Volume 75, Issue 7, pp 3471–3498 | Cite as

Hybrid parallel multimethod hyperheuristic for mixed-integer dynamic optimization problems in computational systems biology

  • Patricia GonzálezEmail author
  • Pablo Argüeso-Alejandro
  • David R. Penas
  • Xoan C. Pardo
  • Julio Saez-Rodriguez
  • Julio R. Banga
  • Ramón Doallo
Article

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.

Keywords

Reverse engineering Computational systems biology Mixed-integer optimization problems Parallel metaheuristics Global optimization Multimethod optimization 

Notes

Acknowledgements

This research received financial support from the Spanish Government through the Projects DPI2017-82896-C2-2-R and TIN2016-75845-P (AEI/FEDER, UE), and from the Galician Government under the Consolidation Program of Competitive Research Units (Network Ref. R2016/045 and Project Ref. ED431C 2017/04), all of them co-funded by FEDER funds of the EU. We also acknowledge Microsoft Research for being awarded with a sponsored Azure account, and CESGA for the access to their facilities.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Architecture Group, CITICUniversity of A CoruñaA CoruñaSpain
  2. 2.MODESTYA Research Group, IMATUniversity of Santiago de CompostelaSantiago de CompostelaSpain
  3. 3.Faculty of Medicine, Institute for Computational Biomedicine, BioquantHeidelberg UniversityHeidelbergGermany
  4. 4.BioProcess Engineering Group, IIM-CSICSpanish National Research CouncilMadridSpain

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