Abstract: Publication date: November 2019Source: Advances in Engineering Software, Volume 137Author(s): Miroslav Vořechovský, Jan Mašek, Jan Eliáš A method is proposed for the construction of uniformly distributed point sets within a design domain using an analogy to a dynamical system of interacting particles. The possibility of viewing various distance-based optimality criteria as formulas representing the potential energy of a system of charged particles is discussed. The potential energy is employed in deriving the equations of motion of the particles. The particles are either attracted or repelled and dissipative dynamical systems can be simulated to achieve optimal and near-optimal arrangements of points. The design domain is set up as an Nvar-dimensional unit hypercube, with Nvar being the number of variables (factors). The number of points is equal to the number of simulations (levels). The periodicity assumption of the design domain is shown to be an elegant way to obtain statistically uniform coverage of the design domain.The ϕp criterion, which is a generalization of the Maximin criterion, is selected in order to demonstrate its analogy with an N-body system. This criterion guarantees that the points are spread uniformly within the design domain. The solution to such an N-body system is presented. The obtained designs are shown to outperform the existing optimal designs in various types of applications: multidimensional numerical integration, statistical exploration of computer models, reliability analyses of engineering systems, and screenings or exploratory designs for the global optimization/minimization of functions.

Abstract: Publication date: October 2019Source: Advances in Engineering Software, Volume 136Author(s): Marthinus N. Ras, Daniel N. Wilke, Albert A. Groenwold, Schalk Kok In this study we examine the rotational (in)variance of the differential evolution (DE) algorithm. We show that the classic DE/rand/1/bin algorithm, which uses constant mutation and standard crossover, is rotationally variant. We then study a previously proposed rotationally invariant formulation in which the crossover operation takes place in an orthogonal base constructed using Gramm-Schmidt orthogonalization.We propose two new formulations by firstly considering a very simple rotationally invariant formulation using constant mutation and whole arithmetic crossover. This rudimentary formulation performs badly, due to lack of diversity. We introduce diversity into the formulation using two distinctly different strategies. The first adjusts the crossover step by perturbing the direction of the linear combination between the target vector and the mutant vector. This formulation is invariant in a stochastic sense only. The other formulation adds a self-scaling random vector with a standard normal distribution, sampled uniformly from the surface of an n-dimensional unit sphere to the unaltered whole arithmetic crossover vector. This formulation is strictly invariant, if in a stochastic sense only.We compare the four invariant formulations in terms of numerical efficiency for a modest set of test problems; the intention not being to propose yet another competitive and/or superior DE variant, but rather to present formulations that are both diverse and invariant, in the hope that this will stimulate additional future contributions, since rotational invariance in general is a desirable, salient feature for an optimization algorithm.

Abstract: Publication date: October 2019Source: Advances in Engineering Software, Volume 136Author(s): Serhii Dranishnykov, Maksym Dosta An advanced approach for saving of simulation results obtained from modelling of granular materials with the discrete element method (DEM) is developed. In order to reduce the volume of generated data, a combination of lossy and lossless compression methods is proposed. This combination allows one to compress simulation results with predefined numerical tolerances. The top-down time-ratio compression algorithm and DEFLATE compression method are used as lossy and lossless methods accordingly. The developed method has been implemented with the C++ programming language and integrated as a subsystem for data storage into DEM simulation framework MUSEN.To estimate the efficiency of the contributed saving approach, several simulation case studies have been selected and analysed. The obtained results have shown that the application of the proposed method makes it possible to reduce the volume of saved data significantly and to avoid the loss of important information.

Abstract: Publication date: October 2019Source: Advances in Engineering Software, Volume 136Author(s): Wei Peng, Weixi Ji Surface mesh quality is one of key factors in finite element applications, since it affects the accuracy and efficiency of finite element simulation and influences the quality of subsequently generated solid mesh. In this paper, we proposed a novel surface mesh smoothing algorithm which aims to produce a more regular mesh from a given input mesh, while accurately preserving its sharp features such as edges and corners. The mesh quality is improved by a combination of surface node classification and node relocation, while the new position of the vertex is obtained by weighted average of the centers of inscribed circles of the surrounding polygon, and projecting the averaged position to lie in space near the input surface mesh. The procedure above is repeated until the maximal change of mesh quality is less than a pre-defined threshold. Finally, several experimental results are presented to demonstrate the effectiveness and practicability of our method.

Abstract: Publication date: October 2019Source: Advances in Engineering Software, Volume 136Author(s): Martin Biel, Arda Aytekin, Mikael Johansson We present POLO.jl — a Julia package that helps algorithm developers and machine-learning practitioners design and use state-of-the-art parallel optimization algorithms in a flexible and efficient way. POLO.jl extends our C++ library POLO, which has been designed and implemented with the same intentions. POLO.jl not only wraps selected algorithms in POLO and provides an easy mechanism to use data manipulation facilities and loss function definitions in Julia together with the underlying compiled C++ library, but it also uses the policy-based design technique in a Julian way to help users prototype optimization algorithms from their own building blocks. In our experiments, we observe that there is little overhead when using the compiled C++ code directly within Julia. We also notice that the performance of algorithms implemented in pure Julia is comparable with that of their C++ counterparts. Both libraries are hosted on GitHub1under the free MIT license, and can be used easily by pulling the pre-built 64-bit architecture Docker images.2

Abstract: Publication date: Available online 1 July 2019Source: Advances in Engineering SoftwareAuthor(s): Mohamed Amine Bouhlel, John T. Hwang, Nathalie Bartoli, Rémi Lafage, Joseph Morlier, Joaquim R.R.A. Martins The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods. SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to training data. It also includes unique surrogate models: kriging by partial least-squares reduction, which scales well with the number of inputs; and energy-minimizing spline interpolation, which scales well with the number of training points. The efficiency and effectiveness of SMT are demonstrated through a series of examples. SMT is documented using custom tools for embedding automatically tested code and dynamically generated plots to produce high-quality user guides with minimal effort from contributors. SMT is maintained in a public version control repository.1

Abstract: Publication date: Available online 19 January 2007Source: Advances in Engineering SoftwareAuthor(s): T.T. Tanyimboh, A.B. TemplemanThis article has been removed consistent with Elsevier Policy on Article Withdrawal. The Publisher apologises for any inconvenience this may cause.