The Problem of measuring the diversity of a non-dominated set and solution

Una propiedad muy importante de los conjuntos no dominados es su diversidad. Mientras mayor sea la diversidad, más rica es la información sobre las posibles soluciones a un problema multiobjetivo. Desde el inicio de la computación evolutiva multiobjetivo se han encontrado dificultades para evaluar la diversidad de los conjuntos no dominados. Muchas métricas diseñadas con este fin, fallan en ejemplos muy sencillos. En este trabajo revisamos en que consisten las principales fallas de las métricas de diversidad y damos una propuesta que no presenta estos problemas. Nuestra propuesta mide la diversidad de una forma diferente, considerando un hiper-volumen de influencia del conjunto, y tiene un comportamiento excelente como medida de desempeño. Se probró nuestra métrica usando un benchmark publicado en la bibliografía, teniendo un desempeño perfecto.Diversity is a very important property for non-dominated sets. The diversity is a measure of how much information is contained in a non-dominated set. Evaluating diversity has been a diffcult issue in multi-objective evolutionary computation. Many diversity performance measures fail in simple cases. In this work, we describe the most common problems in diversity performance measures and we propose a more robust approach. The problem with most performance measures is that they consist on evaluating the standard deviation of the distances between the elements of the non-dominated sets, or a similar calculation. This dependence on a standard deviation produces a high sensibility to small changes in the non-dominated sets. Our approach is based on an hype-volume associated to the non-dominated set. The behavior of this hyper-volume is exactly what we expect from a diversity performance measure. We tested our approach using a benchmark published in bibliography, showing an exceptional performance.Peer Reviewe

Designing EDAs by using the Elitist Convergent EDA Concept and the Boltzmann Distribution

ABSTRACT This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs

Análisis de problemas de choque e impacto entre sólidos deformables por el Método de los Elementos Finitos

El trabajo que se expone ha hecho uso de los últimos avances en mecánica computacional, métodos numéricos, visualización y algoritmos de cálculo para obtener un programa de ordenador para simulación de problemas de choque e impacto de interés práctico para una amplia variedad de sectores industriales. En particular, el objetivo del proyecto SIMPACT ha sido el desarrollo de un paquete de software para análisis de problemas de dinámica rápida y análisis no lineal procesable en una amplia variedad de ordenadores, con aplicación a choques de vehículos, impacto en estructuras aeroespaciales, defensa y problemas de conformado, entre otros.Postprint (published version

Evolutionary computation for wind farm layout optimization

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of five generated wind farms based on a simplified cost of energy evaluation function of the wind farm layouts. Online and offline APIs were implemented in C++, Java, Matlab and Python for this competition to offer a common framework for the competitors. The top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation, the research field of the conference at which the competition was held. Competitors were able to downscale the optimization problem size (number of parameters) by casting the wind farm layout problem as a geometric optimization problem. This strongly reduces the number of evaluations (limited in the scope of this competition) with extremely promising results

Summary of evolutionary computation for wind farm layout optimization

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with optimizing the layouts of ve generated wind farms based on a sim-plied cost of energy evaluation function of the wind farm layouts. Online and oine APIs were implemented in C++, Java, Matlab and Python for this competition to oer a common framework for the competitors. e top four approaches out of eight participating teams are presented in this paper and their results are compared. All of the competitors' algorithms use evolutionary computation

Solution of finite element problems using hybrid parallelization with MPI and OpenMP Solution of finite element problems using hybrid parallelization with MPI and OpenMP

<p>The Finite Element Method (FEM) is used to solve problems like solid deformation and heat diffusion in domains with complex geometries. This kind of geometries requires discretization with millions of elements; this is equivalent to solve systems of equations with sparse matrices and tens or hundreds of millions of variables. The aim is to use computer clusters to solve these systems. The solution method used is Schur substructuration. Using it is possible to divide a large system of equations into many small ones to solve them more efficiently. This method allows parallelization. MPI (Message Passing Interface) is used to distribute the systems of equations to solve each one in a computer of a cluster. Each system of equations is solved using a solver implemented to use OpenMP as a local parallelization method.</p><br><p>The Finite Element Method (FEM) is used to solve problems like solid deformation and heat diffusion in domains with complex geometries. This kind of geometries requires discretization with millions of elements; this is equivalent to solve systems of equations with sparse matrices and tens or hundreds of millions of variables. The aim is to use computer clusters to solve these systems. The solution method used is Schur substructuration. Using it is possible to divide a large system of equations into many small ones to solve them more efficiently. This method allows parallelization. MPI (Message Passing Interface) is used to distribute the systems of equations to solve each one in a computer of a cluster. Each system of equations is solved using a solver implemented to use OpenMP as a local parallelization method.</p

PASSSS: An Implementation of a Novel Diversity Strategy for Handling Constraints

Abstract — In this paper, we introduce PASSSS (P AS 4), th

Design and Optimization of Tunnel Boring Machines by Simulating the Cutting Rock Process using the Discrete Element Method

Abstract. Nowadays there is a large number of tunneling projects in progress, mainly in Europe, both for roads and transport supplies. Performance prediction of tunnel boring machines (TBM) and the determination of some design parameters have become crucial, as they are critical elements in planning a project of mechanical excavation. In this paper we use the Discrete Element Method (DEM) to build models which simulate the rock cutting process under a cutting disk and measure the interaction between forces and hard rock essential in the design of TBM. The DEM is an appropriate tool for modeling geomaterials; it is assumed that a solid material can be represented by a collection of rigid particles interacting with each other in the normal and tangential directions. The particles are linked by cohesive forces which can break and simulate fracture propagation

Cálculo de frecuencias naturales para vigas elásticas con efecto de cortante e inercia rotacional. Caso empotrado articulado

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Cálculo de frecuencias naturales para vigas elásticas con efecto de cortante e inercia rotacional. Caso empotrado articulado

Peer Reviewe