Regularization-free multicriteria optimization of polymer viscoelasticity model

This paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains.This work is funded by National Funds through FCT - Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020 and the European project MSCA-RISE-2015, NEWEX, Reference 734205

General subpopulation framework and taming the conflict inside populations

Structured evolutionary algorithms have been investigated for some time. However,\ud they have been under explored especially in the field of multi-objective optimization.\ud Despite good results, the use of complex dynamics and structures keep the understanding\ud and adoption rate of structured evolutionary algorithms low. Here, we propose a\ud general subpopulation framework that has the capability of integrating optimization\ud algorithms without restrictions as well as aiding the design of structured algorithms.\ud The proposed framework is capable of generalizing most of the structured evolutionary\ud algorithms, such as cellular algorithms, island models, spatial predator-prey, and\ud restricted mating based algorithms. Moreover, we propose two algorithms based on\ud the general subpopulation framework, demonstrating that with the simple addition\ud of a number of single-objective differential evolution algorithms for each objective,\ud the results improve greatly, even when the combined algorithms behave poorly when\ud evaluated alone at the tests. Most importantly, the comparison between the subpopulation\ud algorithms and their related panmictic algorithms suggests that the competition\ud between different strategies inside one population can have deleterious consequences\ud for an algorithm and reveals a strong benefit of using the subpopulation framework

Enhanced Van der Waals calculations in genetic algorithms for protein structure prediction

Several ab initio computational methods for protein structure prediction have been designed using full-atom models and force field potentials to describe interactions among atoms. Those methods involve the solution of a combinatorial problem with a huge search space. Genetic algorithms (GAs) have shown significant performance increases for such methods. However, even a small protein may require hundreds of thousands of energy function evaluations making GAs suitable only for the prediction of very small proteins. We propose an efficient technique to compute the van der Waals energy (the greatest contributor to protein stability) speeding up the whole GA. First, we developed a Cell-List Reconstruction procedure that divides the tridimensional space into a cell grid for each new structure that the GA generates. The cells restrict the calculations of van der Waals potentials to ranges in which they are significant, reducing the complexity of such calculations from quadratic to linear. Moreover, the proposal also uses the structure of the cell grid to parallelize the computation of the van der Waals energy, achieving additional speedup. The results have shown a significant reduction in the run time required by a GA. For example, the run time for the prediction of a protein with 147,980 atoms can be reduced from 217 days to 7 h.FAPESPCAPESCNP

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service‐Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects.Conselho Nacional de Desenvolvimento Científico e Tecnológico. Grant Number: 165009/2015-2 Fundação de Amparo à Pesquisa do Estado de São Paulo (at CEMEAI). Grant Number: 2013/07375-