Tools for traffic engineering on IP networks

In this work, an user friendly software application is proposed, built on top of a network optimization framework, aiming to make traffic engineering an easier task for IP network administrators. This framework was developed in the Center of Computer Science and Technology (CCTC) of the University of Minho and allows the improvement of quality of service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols, such as OSPF. This goal is achieved mainly using Evolutionary Algorithms as the optimization engines, while networks are represented using graph-based mathematical models. These methods allow the optimization of distinct cost functions, using penalties that take into account several measures of network performance such as network congestion and average end-to-end delays. The main goal of this work is to create a structured graphical user interface to support the optimization framework, enabling the user to simulate the effects of diferente OSPF settings, to obtain highly optimized configurations and to compare different weight setting optimization methods

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

An Integrated System Dynamics Model for Analyzing Behaviour of the Social-Energy-Economic-Climatic System: User’s Manual

The User\u27s Manual is planned to assist the user in (i) understanding the ANEMI model structure; and (ii) learning how to use the model for policy simulation. ANEMI model is a research product and is not developed as a commercial software. This manual contains a brief description of the main features of the Vensim system dynamics simulation software (Ventana, 2010), as well as integrated simulationoptimization procedure developed by incorporating MATLAB (MathWorks, 2007) functionalities with Vensim system dynamics simulation. With the help of Vensim and MATLAB software packages, the user can use, modify and/or run the ANEMI models provided with the manual. The step-by-step instructions are provided for using ANEMI model for policy simulation. Advanced features of the ANEMI model, such as subscripting (arrays), linking external functionality to implement optimization within simulation, are presented using ANEMI simulation models as an example to accelerate the learning process. This manual also contains a detailed description of DLL (Dynamic-Link Library) file generation procedure by Visual Studio software package (Microsoft, 2008). The full description of the ANEMI model is provided in Akhtar et al (2011) available on the CD-ROM.https://ir.lib.uwo.ca/wrrr/1038/thumbnail.jp

Generation and validation of optimal topologies for solid freeform fabrication

The study of fabricating topologically optimized parts is presented hereafter. The mapping of topology optimization results for Standard Tessellation Language (STL) writing would enable the solid freeform fabrication of lightweight mechanisms. Aerospace leaders such as NASA, Boeing, Airbus, European Aeronautic Defense And Space Company (EADS), and GE Aero invest in topology optimization research for the production of lightweight materials. Certain concepts such as microstructural homogenization, discretization, and mapping are reviewed and presented in the context of topology optimization. Future biomedical applications of solid freeform fabrication such as organ printing stand to save millions of lives through the robust development of optimized technology. The ability of topologically optimized parts to perform mechanically is presented using FEA and compression testing. A comprehensive user input/output topology optimization software results from the investigation. Functions such as accepting any user design volume, loading, constraining, performing optimization, scaling, and writing an STL file are coalesced into one program named optstl. The pre-existing publicly available software packages have been primarily for graphical use, such as 3D plots, and thus cannot be directly interfaced with solid freeform fabrication technology. The reduction of multiple software interfaces into a simplified MATLAB program and the ability to write STL files of topologically optimized models provides scientists and engineers this interfacing ability. The results of this study are evaluated using finite element analysis (FEA), compression testing, and statistical testing --Abstract, page iii

Moving Horizon Estimation for JModelica.org

In this thesis a Moving Horizon Estimator (MHE) has been implemented for the JModelica.org platform. JModelica.org is an open-source software platform for simulation and optimization of systems described in the modeling language Modelica. MHE is an optimization-based strategy for state estimation where, at each time step, a finite horizon optimization problem is solved to generate an estimate of the current state values. The goal has been to implement an MHE that works with many already existing Modelica models and that has an intuitive user interface. The performance of the implemented MHE is evaluated using both linear and nonlinear systems in a series of simulation examples. The results indicate that the MHE performs well

Swarm-CG: Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

We present Swarm-CG, a versatile software for the automatic iterative parametrization of bonded parameters in coarse-grained (CG) models, ideal in combination with popular CG force fields such as MARTINI. By coupling fuzzy self-tuning particle swarm optimization to Boltzmann inversion, Swarm-CG performs accurate bottom-up parametrization of bonded terms in CG models composed of up to 200 pseudo atoms within 4-24 h on standard desktop machines, using default settings. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of complex molecular systems interesting for bio- and nanotechnology. Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity, and size. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Demonstration data are available at: www.github.com/GMPavanLab/SwarmCG