Temperature management for heterogeneous multi-core FPGAs using adaptive evolutionary multi-objective approaches

Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionary algorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.

Optimization of three-dimensional branching networks of microchannels for thermal management of microelectronics

The aim of this work is to present a methodology to develop cost-effective thermal management solutions for microelectronic devices, capable of removing maximum amount of heat and delivering maximally uniform temperature distributions. The topological and geometrical characteristics of multiple-story three-dimensional branching networks of microchannels were developed using multi-objective optimization. A conjugate heat transfer analysis software package and an automatic 3D microchannel network generator were developed and coupled with a modified version of a particle-swarm optimization algorithm with a goal of creating a design tool for 3D networks of optimized coolant flow passages. Numerical algorithms in the conjugate heat transfer solution package include a quasi-ID thermo-fluid solver and a steady heat diffusion solver, which were validated against results from high-fidelity Navier-Stokes equations solver and analytical solutions for basic fluid dynamics test cases. Pareto-optimal solutions demonstrate that thermal loads of up to 500 W/cm2 can be managed with 3D microchannel networks, with pumping power requirements up to 50% lower with respect to currently used high-performance cooling technologies.

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Direct multisearch for multiobjective optimization

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functions. Our framework is inspired by the search/poll paradigm of direct-search methods of directional type and uses the concept of Pareto dominance to maintain a list of nondominated points (from which the new iterates or poll centers are chosen). The aim of our method is to generate as many points in the Pareto front as possible from the polling procedure itself, while keeping the whole framework general enough to accommodate other disseminating strategies, in particular, when using the (here also) optional search step. DMS generalizes to multiobjective optimization (MOO) all direct-search methods of directional type. We prove under the common assumptions used in direct search for single objective optimization that at least one limit point of the sequence of iterates generated by DMS lies in (a stationary form of) the Pareto front. However, extensive computational experience has shown that our methodology has an impressive capability of generating the whole Pareto front, even without using a search step. Two by-products of this paper are (i) the development of a collection of test problems for MOO and (ii) the extension of performance and data profiles to MOO, allowing a comparison of several solvers on a large set of test problems, in terms of their efficiency and robustness to determine Pareto fronts.

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Multiobjective optimization for node adaptation in the analysis of composite plates using a meshless collocation method

The bending of simply supported composite plates is analyzed using a direct collocation meshless numerical method. In order to optimize node distribution the Direct MultiSearch (DMS) for multi-objective optimization method is applied. In addition, the method optimizes the shape parameter in radial basis functions. The optimization algorithm was able to find good solutions for a large variety of nodes distribution.

Multiobjective optimization of cold-formed steel columns

The optimal design of cold-formed steel columns is addressed in this paper, with two objectives: maximize the local-global buckling strength and maximize the distortional buckling strength. The design variables of the problem are the angles of orientation of cross-section wall elements the thickness and width of the steel sheet that forms the cross-section are fixed. The elastic local, distortional and global buckling loads are determined using Finite Strip Method (CUFSM) and the strength of cold-formed steel columns (with given length) is calculated using the Direct Strength Method (DSM). The bi-objective optimization problem is solved using the Direct MultiSearch (DMS) method, which does not use any derivatives of the objective functions. Trade-off Pareto optimal fronts are obtained separately for symmetric and anti-symmetric cross-section shapes. The results are analyzed and further discussed, and some interesting conclusions about the individual strengths (local-global and distortional) are found.

Probabilistic maintenance and optimization strategies for deteriorating civil infrastructures

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

The suitability of steam exploded vitis vinifera and alkaline lignin for the manufacture of fiberboard

The main objective of this study was to explore the suitability of Vitis vinifera as a raw material and alkaline lignin as a natural binder for fiberboard manufacturing. In the first step, Vitis vinifera was steam- exploded through a thermo-mechanical vapor process in a batch reactor, and the obtained pulp was dried, ground, and pressed to produce the boards. The effects of pretreatment factors and pressing conditions on the chemical composition of the fibers and the physico-mechanical properties of binderless fiberboards were evaluated, and the conditions that optimize these properties were found. A response surface method based on a central composite design and multiple-response optimization was used. The variables studied and their respective variation ranges were: pretreatment temperature (Tr: 190-210ºC), pretreatment time (tr: 5-10 min), pressing temperature (Tp: 190-210ºC), pressing pressure (Pp: 8-16MPa), and pressing time (tp: 3-7min). The results of the optimization step show that binderless fiberboards have good water resistance and weaker mechanical properties. In the second step, fiberboards based on alkaline lignin and Vitis vinifera pulp produced at the optimal conditions determined for binderless fiberboards were prepared and their physico-mechanical properties were tested. Our results show that the addition of about 15% alkaline lignin leads to the production of fiberboards that fully meet the requirements of the relevant standard specifications

Tietoverkon varmistusten simulointi ja optimointi

Tässä diplomityössä määritellään varmistusjärjestelmän simulointimalli eli varmistusmalli. Varmistusjärjestelmän toiminta optimoidaan kyseisen varmistusmallin avulla. Optimoinnin tavoitteena on parantaa varmistusjärjestelmän tehokkuutta. Parannusta etsitään olemassa olevien varmistusjärjestelmän resurssien maksimaalisella hyödyntämisellä. Varmistusmalli optimoidaan evoluutioalgoritmin avulla. Optimoinnissa on useita tavoitteita, jotka ovat ristiriidassa keskenään. Monitavoiteoptimointiongelma muunnetaan yhden tavoitteen optimointiongelmaksi muodostamalla tavoitefunktio painotetun summan menetelmän avulla. Rinnakkain edellisen menetelmän kanssa käytetään myös Pareto-optimointia. Pareto-optimaalisen rintaman pisteiden etsintä ohjataan lähelle painotetun summan menetelmän optimipistettä. Evoluutioalgoritmin toteutuksessa käytetään hyväksi varmistusjärjestelmiin liittyvää ongelmakohtaista tietoa. Työn tuloksena saadaan varmistusjärjestelmän simulointi- sekä optimointityökalu. Simulointityökalua käytetään kartoittamaan nykyisen varmistusjärjestelmän toimivuutta. Optimoinnin avulla tehostetaan varmistusjärjestelmän toimintaa. Työkalua voidaan käyttää myös uusien varmistusjärjestelmien suunnittelussa sekä nykyisten varmistusjärjestelmien laajentamisessa.

Conception des réseaux maillés sans fil à multiples-radios multiples-canaux

Généralement, les problèmes de conception de réseaux consistent à sélectionner les arcs et les sommets d’un graphe G de sorte que la fonction coût est optimisée et l’ensemble de contraintes impliquant les liens et les sommets dans G sont respectées. Une modification dans le critère d’optimisation et/ou dans l’ensemble de contraintes mène à une nouvelle représentation d’un problème différent. Dans cette thèse, nous nous intéressons au problème de conception d’infrastructure de réseaux maillés sans fil (WMN- Wireless Mesh Network en Anglais) où nous montrons que la conception de tels réseaux se transforme d’un problème d’optimisation standard (la fonction coût est optimisée) à un problème d’optimisation à plusieurs objectifs, pour tenir en compte de nombreux aspects, souvent contradictoires, mais néanmoins incontournables dans la réalité. Cette thèse, composée de trois volets, propose de nouveaux modèles et algorithmes pour la conception de WMNs où rien n’est connu à l’ avance. Le premiervolet est consacré à l’optimisation simultanée de deux objectifs équitablement importants : le coût et la performance du réseau en termes de débit. Trois modèles bi-objectifs qui se différent principalement par l’approche utilisée pour maximiser la performance du réseau sont proposés, résolus et comparés. Le deuxième volet traite le problème de placement de passerelles vu son impact sur la performance et l’extensibilité du réseau. La notion de contraintes de sauts (hop constraints) est introduite dans la conception du réseau pour limiter le délai de transmission. Un nouvel algorithme basé sur une approche de groupage est proposé afin de trouver les positions stratégiques des passerelles qui favorisent l’extensibilité du réseau et augmentent sa performance sans augmenter considérablement le coût total de son installation. Le dernier volet adresse le problème de fiabilité du réseau dans la présence de pannes simples. Prévoir l’installation des composants redondants lors de la phase de conception peut garantir des communications fiables, mais au détriment du coût et de la performance du réseau. Un nouvel algorithme, basé sur l’approche théorique de décomposition en oreilles afin d’installer le minimum nombre de routeurs additionnels pour tolérer les pannes simples, est développé. Afin de résoudre les modèles proposés pour des réseaux de taille réelle, un algorithme évolutionnaire (méta-heuristique), inspiré de la nature, est développé. Finalement, les méthodes et modèles proposés on été évalués par des simulations empiriques et d’événements discrets.

Gestion adaptative des ressources dans les réseaux maillés sans fil à multiples-radios multiples-canaux

Depuis quelques années, la recherche dans le domaine des réseaux maillés sans fil ("Wireless Mesh Network (WMN)" en anglais) suscite un grand intérêt auprès de la communauté des chercheurs en télécommunications. Ceci est dû aux nombreux avantages que la technologie WMN offre, telles que l'installation facile et peu coûteuse, la connectivité fiable et l'interopérabilité flexible avec d'autres réseaux existants (réseaux Wi-Fi, réseaux WiMax, réseaux cellulaires, réseaux de capteurs, etc.). Cependant, plusieurs problèmes restent encore à résoudre comme le passage à l'échelle, la sécurité, la qualité de service (QdS), la gestion des ressources, etc. Ces problèmes persistent pour les WMNs, d'autant plus que le nombre des utilisateurs va en se multipliant. Il faut donc penser à améliorer les protocoles existants ou à en concevoir de nouveaux. L'objectif de notre recherche est de résoudre certaines des limitations rencontrées à l'heure actuelle dans les WMNs et d'améliorer la QdS des applications multimédia temps-réel (par exemple, la voix). Le travail de recherche de cette thèse sera divisé essentiellement en trois principaux volets: le contrôle d‟admission du trafic, la différentiation du trafic et la réaffectation adaptative des canaux lors de la présence du trafic en relève ("handoff" en anglais). Dans le premier volet, nous proposons un mécanisme distribué de contrôle d'admission se basant sur le concept des cliques (une clique correspond à un sous-ensemble de liens logiques qui interfèrent les uns avec les autres) dans un réseau à multiples-sauts, multiples-radios et multiples-canaux, appelé RCAC. Nous proposons en particulier un modèle analytique qui calcule le ratio approprié d'admission du trafic et qui garantit une probabilité de perte de paquets dans le réseau n'excédant pas un seuil prédéfini. Le mécanisme RCAC permet d‟assurer la QdS requise pour les flux entrants, sans dégrader la QdS des flux existants. Il permet aussi d‟assurer la QdS en termes de longueur du délai de bout en bout pour les divers flux. Le deuxième volet traite de la différentiation de services dans le protocole IEEE 802.11s afin de permettre une meilleure QdS, notamment pour les applications avec des contraintes temporelles (par exemple, voix, visioconférence). À cet égard, nous proposons un mécanisme d'ajustement de tranches de temps ("time-slots"), selon la classe de service, ED-MDA (Enhanced Differentiated-Mesh Deterministic Access), combiné à un algorithme efficace de contrôle d'admission EAC (Efficient Admission Control), afin de permettre une utilisation élevée et efficace des ressources. Le mécanisme EAC prend en compte le trafic en relève et lui attribue une priorité supérieure par rapport au nouveau trafic pour minimiser les interruptions de communications en cours. Dans le troisième volet, nous nous intéressons à minimiser le surcoût et le délai de re-routage des utilisateurs mobiles et/ou des applications multimédia en réaffectant les canaux dans les WMNs à Multiples-Radios (MR-WMNs). En premier lieu, nous proposons un modèle d'optimisation qui maximise le débit, améliore l'équité entre utilisateurs et minimise le surcoût dû à la relève des appels. Ce modèle a été résolu par le logiciel CPLEX pour un nombre limité de noeuds. En second lieu, nous élaborons des heuristiques/méta-heuristiques centralisées pour permettre de résoudre ce modèle pour des réseaux de taille réelle. Finalement, nous proposons un algorithme pour réaffecter en temps-réel et de façon prudente les canaux aux interfaces. Cet algorithme a pour objectif de minimiser le surcoût et le délai du re-routage spécialement du trafic dynamique généré par les appels en relève. Ensuite, ce mécanisme est amélioré en prenant en compte l‟équilibrage de la charge entre cliques.