Multimethod optimization in the cloud: A case-study in systems biology modelling: Multimethod optimization in the Cloud

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.