Multimethod optimization in the cloud: A case‐study in systems biology modelling
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Multimethod optimization in the cloud: A case‐study in systems biology modellingData
2018-06-25Cita bibliográfica
González, P., Penas, D. R., Pardo, X. C., Banga, J. R., & Doallo, R. (2018). Multimethod optimization in the cloud: A case‐study in systems biology modelling. Concurrency and Computation: Practice and Experience, 30(12), e4488.
Resumo
[Abstract] Optimization problems appear in many different applications in science and engineering. A large number of different algorithms have been proposed for solving them; however, there is no unique general optimization method that performs efficiently across a diverse set of problems. Thus, a multimethod optimization, in which different algorithms cooperate to outperform the results obtained by any of them in isolation, is a very appealing alternative. Besides, as real‐life optimization problems are becoming more and more challenging, the use of HPC techniques to implement these algorithms represents an effective strategy to speed up the time‐to‐solution. In addition, a parallel multimethod approach can benefit from the effortless access to q large number of distributed resources facilitated by cloud computing. In this paper, we propose a self‐adaptive cooperative parallel multimethod for global optimization. This proposal aims to perform a thorough exploration of the solution space by means of multiple concurrent executions of a broad range of search strategies. For its evaluation, we consider an extremely challenging case‐study from the field of computational systems biology. We also assess the performance of the proposal on a public cloud, demonstrating both the potential of the multimethod approach and the opportunity that the cloud provides for these problems.
Palabras chave
cloud computing
hybrid programming
Microsoft Azure
multimethod optimization
parallel metaheuristics
hybrid programming
Microsoft Azure
multimethod optimization
parallel metaheuristics
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Dereitos
This is the peer reviewed version of the following article: González, P., Penas, D. R., Pardo, X. C., Banga, J. R., & Doallo, R. (2018). Multimethod optimization in the cloud: A case‐study in systems biology modelling. Concurrency and Computation: Practice and Experience, 30(12), e4488., which has been published in final form at https://doi.org/10.1002/cpe.4488. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
ISSN
1532-0626
1532-0634
1532-0634