A survey of parallel hybrid applications to the permutation flow shop scheduling problem and similar problems

Parallel algorithms have focused an increased interest due to advantages in computation time and quality of solutions when applied to industrial engineering problems. This communication is a survey and classification of works in the field of hybrid algorithms implemented in parallel and applied to combinatorial optimization problems similar to the to the permutation flowshop problem with the objective of minimizing the makespan, Fm|prmu|Cmax according to the Graham notation, the travelling salesman problem (TSP), the quadratic assignment problem (QAP) and, in general, those whose solution can be expressed as a permutation

О Классификации Приближенных Методов Комбинаторной Оптимизации

В работе предлагается классификация приближенных методов комбинаторной оптимизации, которая обобщает и дополняет существующие подходы

DANTE - The combination between an ant colony optimization algorithm and a depth search method

The ε-DANTE method is an hybrid meta-heuristic. In combines the evolutionary Ant Colony Optimization (ACO) algorithms with a limited Depth Search. This Depth Search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-Degree Spanning Trees problem, proving the effectiveness of the method when compared with other already tested evolutionary methods. © 2008 IEEE

Hiperheurística diseñada para un problema de localización y transporte público

Se propone aquí el empleo de una hiperheurística para resolver un problema de localización y transporte. El trabajo presenta una clasificación en el campo de las hiperheurísticas, se establecen claramente los beneficios que proporcionan y se exponen las nuevas tendencias en su utilización. Se plantea un modelo de una hiperheurística aleatoria basada en metaheurísticas. Las metaheurísticas empleadas en el modelo son: Recocido Simulado (SA: Simulated Annealing) y Optimización por Colonia de Hormigas (ACO: Ant Colony Optimization). Se destacan las debilidades y fortalezas que éstas presentan, y se hace hincapié en la importancia de la calibración de los parámetros asociados. Se propone un simple algoritmo que resuelve una instancia basada en una línea existente de transporte público de pasajeros. Se demuestra que la hiperheurística obtiene resultados satisfactorios, eligiendo aleatoriamente la técnica a utilizar en cada iteración. Así, las técnicas logran combinarse para obtener un equilibrio entre la diversificación y la intensificación en la búsqueda de soluciones. Esto implica disminuir la cantidad de evaluaciones a realizar y mejorar los tiempos de cómputos para la obtención de una solución satisfactoria.Fil: Rodriguez, Diego Alejandro. Universidad Nacional de Salta. Facultad de Cs.exactas - Cons.de Investigacion; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Grupo Vinculado Al Plapiqui - Investigación y Desarrollo en Tecnologia Quimica; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional de la Patagonia Austral. Unidad Academica Caleta Olivia. Departamento de Cs.exactas y Naturales; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Brignole, Nelida Beatriz. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnológico Bahia Blanca. Planta Piloto de Ingenieria Quimica (i). Grupo Vinculado Al Plapiqui - Investigación y Desarrollo en Tecnologia Quimica; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentin

Розроблення математичного апарату експертної системи для моделювання рецептур морозива із заданими показниками якості

Вступ. Застосування нових методів, зокрема експертних систем з математичним апаратом, дозволяє удосконалювати рецептурний склад багатокомпонентних харчових продуктів в широкому діапазоні вмісту складових компонентів з їх повною або частковою заміною на інші, зокрема й на натуральні функціонально-технологічні інгредієнти. Проблематика. Створення та використання гібридної експертної системи моделювання рецептур морозива не можливо здійснити без застосування особливого математичним апарату. Мета. Розробка математичних моделей та методів, які дозволяють оперативно розраховувати багатокомпонентні рецептури морозива з нормативним хімічним складом з урахуванням наявної на підприємстві сировини й функціонально-технологічних інгредієнтів та одержувати готовий продукт гарантованої якості. Матеріали й методи. Використано метод аналізу й синтезу, узагальнення та наукової абстракції, а також метод математичного моделювання. Інформаційною базою дослідження слугували результати лабораторних досліджень якості рецептурних компонентів та морозива різного хімічного складу. Математичне моделювання з використанням кортежів, систем рівнянь та обмежень детально здійснено у програмних пакетах MathCad та MathLab. Результати. В ході розробки математичного апарату експертної системи було одержано теоретико-множинну математичну модель управління якістю готового продукту на етапі оперативного планування рецептури нових видів морозива підвищеної харчової цінності, оптимізовано за складом багатокомпонентні рецептури морозива, сформовано модель визначення оптимального набору керуючих впливів за наявності технологічних дефектів під час розрахунку рецептур. Висновки. Створений математичний апарат для моделювання рецептур морозива є універсальним завдяки взаємозамінності окремих функціонально-технологічних компонентів, що перевірено та підтверджено під час його апробації у науково-дослідних лабораторіях.Introduction. Application of new methods, in particular, expert systems with mathematical apparatus, enables improving the recipe composition of multi-component food products in a wide range of content of components with their full or partial replacement by alternative ones, including natural functional and technological ingredients. Problem Statement. The creation and use of hybrid expert system of ice cream recipe modelling is impossible without using special mathematical apparatus. Purpose. To develop mathematical models and methods that enable to calculate the multicomponent ice cream recipes with the standard chemical composition taking into account the raw materials and functional and technological ingredients available at the manufacturer and to get the finished products of guaranteed quality. Materials and Methods. The methods of analysis and synthesis, generalization and scientific abstraction, as well as the method of mathematical modelling are used. The information base of the research is the results of laboratory studies of the quality of recipe components and ice creams of different chemical composition. Mathematical modelling with the use of tuples, systems of equations and restrictions, is made in MathCad and MathLab software packages. Results. As a result of the development of the expert system mathematical apparatus, a set-theoretical mathematical model of the finished product quality control at the stage of operative planning of new types of ice cream with increased nutritional value has been obtained; multi-component ice cream recipes have been optimized in terms of composition; and a model for determining the optimal set of control actions in the presence of technological defects in the calculation of recipes has been built. Conclusions. The created mathematical apparatus for modeling ice cream recipes has a large-scale application due to interchangeability of separate functional and technological components, which has been tested and confirmed during the trials in research laboratories.Введение. Применение новых методов, в частности экспертных систем с математическим аппаратом, позволяет совершенствовать рецептурный состав многокомпонентных пищевых продуктов в широком диапазоне содержания составляющих компонентов с их полной или частичной заменой на другие, в том числе и на натуральные функционально-технологические ингредиенты. Проблематика. Создание и использование гибридной экспертной системы моделирования рецептур мороженого невозможно осуществить без использования особого математического аппарата. Цель. Разработка математических моделей и методов, которые позволяют оперативно рассчитывать много компонентные рецептуры мороженого с нормативным химическим составом с учетом имеющихся на предприятии сырья и функционально-технологических ингредиентов и получать готовый продукт гарантированного качества. Материалы и методы. Использован метод анализа и синтеза, обобщения и научной абстракции, а также метод математического моделирования. Информационной базой исследования послужили результаты лабораторных исследований качества рецептурных компонентов и мороженого разного химического состава. Математическое моделирование с использованием кортежей, систем уравнений и ограничений подробно осуществлено в программных пакетах MathCad и MathLab. Результаты. В ходе разработки математического аппарата экспертной системы была получена теоретико-множественная математическая модель управления качеством готового продукта на этапе оперативного планирования рецептуры новых видов мороженого повышенной пищевой ценности, оптимизировано по составу многокомпонентные рецептуры мороженого, сформирована модель определения оптимального набора управляющих воздействий при наличии технологических дефектов при расчете рецептур. Выводы. Созданный математический аппарат для моделирования рецептур мороженого является универсальным благодаря взаимозаменяемости отдельных функционально-технологических компонентов, что проверено и подтверждено во время его апробации в научно-исследовательских лабораториях

Decomposition, Reformulation, and Diving in University Course Timetabling

In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft constraint violation. The goal is then to minimise a linear combination of such measures. This paper studies an approach to such problems, which can be thought of as multiphase exploitation of multiple objective-/value-restricted submodels. In this approach, only one computationally difficult component of a problem and the associated subset of objectives is considered at first. This produces partial solutions, which define interesting neighbourhoods in the search space of the complete problem. Often, it is possible to pick the initial component so that variable aggregation can be performed at the first stage, and the neighbourhoods to be explored next are guaranteed to contain feasible solutions. Using integer programming, it is then easy to implement heuristics producing solutions with bounds on their quality. Our study is performed on a university course timetabling problem used in the 2007 International Timetabling Competition, also known as the Udine Course Timetabling Problem. In the proposed heuristic, an objective-restricted neighbourhood generator produces assignments of periods to events, with decreasing numbers of violations of two period-related soft constraints. Those are relaxed into assignments of events to days, which define neighbourhoods that are easier to search with respect to all four soft constraints. Integer programming formulations for all subproblems are given and evaluated using ILOG CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.This work was supported by the Spanish MICINN and AEI, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53 and grant TIN2016-80565-R

Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study

[EN] Virtual Screening (VS) methods can considerably aid clinical research by predicting how ligands interact with pharmacological targets, thus accelerating the slow and critical process of finding new drugs. VS methods screen large databases of chemical compounds to find a candidate that interacts with a given target. The computational requirements of VS models, along with the size of the databases, containing up to millions of biological macromolecular structures, means computer clusters are a must. However, programming current clusters of computers is no easy task, as they have become heterogeneous and distributed systems where various programming models need to be used together to fully leverage their resources. This paper evaluates several strategies to provide peak performance to a GPU-based molecular docking application called METADOCK in heterogeneous clusters of computers based on CPU and NVIDIA Graphics Processing Units (GPUs). Our developments start with an OpenMP, MPI and CUDA METADOCK version as a baseline case of cluster utilization. Next, we explore the virtualized GPUs provided by the rCUDA framework in order to facilitate the programming process. rCUDA allows us to use remote GPUs, i.e. installed in other nodes of the cluster, as if they were installed in the local node, so enabling access to them using only OpenMP and CUDA. Finally, several load balancing strategies are analyzed in a search to enhance performance. Our results reveal that the use of middleware like rCUDA is a convincing alternative to leveraging heterogeneous clusters, as it offers even better performance than traditional approaches and also makes it easier to program these emerging clusters.This work is jointly supported by the Fundacion Seneca (Agencia Regional de Ciencia y Tecnologia, Region de Murcia) under grant 18946/JLI/13, and by the Spanish MEC and European Commission FEDER under grants TIN2015-66972-C5-3-R and TIN2016-78799-P (AEI/FEDER, UE). We also thank NVIDIA for hardware donation under GPU Educational Center 2014-2016 and Research Center 2015-2016. Furthermore, researchers from Universitat Politecnica de Valencia are supported by the Generalitat Valenciana under Grant PROMETEO/2017/077. Authors are also grateful for the generous support provided by Mellanox Technologies Inc.Imbernón, B.; Prades Gasulla, J.; Gimenez Canovas, D.; Cecilia, JM.; Silla Jiménez, F. (2018). Enhancing large-scale docking simulation on heterogeneous systems: An MPI vs rCUDA study. Future Generation Computer Systems. 79:26-37. https://doi.org/10.1016/j.future.2017.08.050S26377

Tuning Genetic Algorithm Parameters to Improve Convergence Time

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation of S. cerevisiae altogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters-generation gap, crossover, and mutation rates has-been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy

METADOCK: A parallel metaheuristic schema for virtual screening methods

Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The application decides the optimization technique at running time by setting a configuration schema. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.Ingeniería, Industria y Construcció