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

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 messagepassing (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 (MIDO) problems. In particular, three very challenging problems from the domain of dynamic modelling 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.

Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications

“…We have extensive experience in the parallelization of different metaheuristics using different strategies (González et al, 2018, 2019; Pardo et al, 2020; Penas et al, 2015, 2015b; Penas et al, 2017; Teijeiro et al, 2016,b). Based on this previous experience, we have proposed in González et al (2022), a parallel ACO algorithm adapted to a particular binary combinatorial problem, the signaling of cellular networks.…”

Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications

“…We have extensive experience in the parallelization of different metaheuristics using different strategies [47,48,49,50,51,52,53,54]. Based on this previous experience, we have proposed in [27] a parallel ACO algorithm adapted to a particular binary combinatorial problem, the signaling of cellular networks.…”

Parallel ant colony optimization for the training of cell signaling networks

“…In Pardo, Argüeso-Alejandro, González, Banga, and Doallo (2020), a cloud-based implementation in Spark was proposed for the Scatter Search method. Furthermore, in González et al (2019) a hybrid parallel multimethod prototype was proposed that exploit multiple computational resources to perform simultaneous searches using different metaheuristics.…”