Modeling and Control of a Flexible Structure Incorporating Inertial Slip-Stick Actuators

Shape and vibration control of a linear flexible structure by means of a new type of inertial slip-stick actuator are investigated. A nonlinear model representing the interaction between the structure and a six-degree-of-freedom Stewart platform system containing six actuators is derived, and closed-loop stability and performance of the controlled systems are investigated. A linearized model is also derived for design purposes. Quasistatic alignment of a payload attached to the platform is solved simply by using a proportional controller based on a linear kinematic model. The stability of this controller is examined using a dynamic model of the complete system and is validated experimentally by introducing random thermal elongations of several structural members. Vibration control is solved using an H∞ loop-shaping controller and, although its performance is found to be less satisfactory than desired, the nonlinear model gives good predictions of the performance and stability of the closed-loop system

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Metaheuristic approaches for the quartet method of hierarchical clustering

Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several metaheuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search metaheuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times

A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture

A two-stage stochastic programming with recourse model for the problem of determining optimal planting plans for a vegetable crop is presented in this paper. Uncertainty caused by factors such as weather on yields is a major influence on many systems arising in horticulture. Traditional linear programming models are generally unsatisfactory in dealing with the uncertainty and produce solutions that are considered to involve an unacceptable level of risk. The first stage of the model relates to finding a planting plan which is common to all scenarios and the second stage is concerned with deriving a harvesting schedule for each scenario. Solutions are obtained for a range of risk aversion factors that not only result in greater expected profit compared to the corresponding deterministic model, but also are more robust

Variable neighbourhood search for the minimum labelling Steiner tree problem

We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running time

Constructive Heuristics for the Minimum Labelling Spanning Tree Problem: a preliminary comparison

This report studies constructive heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree that uses edges that are as similar as possible. Given an undirected labeled connected graph (i.e., with a label or color for each edge), the minimum labeling spanning tree problem seeks a spanning tree whose edges have the smallest possible number of distinct labels. The model can represent many real-world problems in telecommunication networks, electric networks, and multimodal transportation networks, among others, and the problem has been shown to be NP-complete even for complete graphs. A primary heuristic, named the maximum vertex covering algorithm has been proposed. Several versions of this constructive heuristic have been proposed to improve its efficiency. Here we describe the problem, review the literature and compare some variants of this algorithm

Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics

Extending Archives: Folklife, Social History, and the Work of R. Henderson Shuffler

R. Henderson Shuffier set the historical record straight. Throughout his career, this self-proclaimed myth-killer \u27 urged Texans, and anyone else who would listen, to reconsider what it meant to be Texan and how to study Texas history. As curator of the University of Texas\u27s Texana Program and later as the first director of the Institute ofTexan Cultures in San Antonio, Shuffier expanded the traditional scope of Texas history beyond political, economic, and military achievements and presented a more complete, unbiased picture of the state\u27s heritage that included groups previously underrepresented in historical and public discourse. At a time when academia was witnessing a significant methodological shift toward a new social history, Shuffier implemented his own unique approach to documentation, access, and public education, combining aspects of social history and folklife studies in an attempt to create a new public image for Texas\u27s historical resources. From the early 1960s to the mid-l 970s, he helped shape these two institutions into first-rate repositories and exhibits of Texana and folk culture. With an emphasis on making history personally relevant to the public, Shuffler devised innovative ways to entertain, engage, and-most importantly-educate, striking a balance between archives and traditional museums. This article will discuss the implications of Shuffler\u27s approach for archivists working today and will explore the impact of social history and folklife on the archival profession

Solving the minimum labelling spanning tree problem using hybrid local search

Given a connected, undirected graph whose edges are labelled (or coloured), the minimum labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest number of distinct labels (or colours). In recent work, the MLST problem has been shown to be NP-hard and some effective heuristics (Modified Genetic Algorithm (MGA) and Pilot Method (PILOT)) have been proposed and analyzed. A hybrid local search method, that we call Group-Swap Variable Neighbourhood Search (GS-VNS), is proposed in this paper. It is obtained by combining two classic metaheuristics: Variable Neighbourhood Search (VNS) and Simulated Annealing (SA). Computational experiments show that GS-VNS outperforms MGA and PILOT. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristic

Neuronal and non-neuronal signals regulate <i>Caernorhabditis elegans</i> avoidance of contaminated food

One way in which animals minimise the risk of infection is to reduce their contact with contaminated food. Here we establish a model of pathogen-contaminated food avoidance using the nematode worm Caernorhabditis elegans. We find that avoidance of pathogen-contaminated food protects C. elegans from the deleterious effects of infection and, using genetic approaches, demonstrate that multiple sensory neurons are required for this avoidance behaviour. In addition, our results reveal that avoidance of contaminated food requires bacterial adherence to non-neuronal cells in the tail of C. elegans that are also required for the cellular immune response. Previous studies in C. elegans have contributed significantly to our understanding of molecular and cellular basis of host-pathogen interactions and our model provides a unique opportunity to gain basic insights into how animals avoid contaminated food