On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Layoutautomatisierung im analogen IC-Entwurf mit formalisiertem und nicht-formalisiertem Expertenwissen

After more than three decades of electronic design automation, most layouts for analog integrated circuits are still handcrafted in a laborious manual fashion today. Obverse to the highly automated synthesis tools in the digital domain (coping with the quantitative difficulty of packing more and more components onto a single chip – a desire well known as More Moore), analog layout automation struggles with the many diverse and heavily correlated functional requirements that turn the analog design problem into a More than Moore challenge. Facing this qualitative complexity, seasoned layout engineers rely on their comprehensive expert knowledge to consider all design constraints that uncompromisingly need to be satisfied. This usually involves both formally specified and nonformally communicated pieces of expert knowledge, which entails an explicit and implicit consideration of design constraints, respectively. Existing automation approaches can be basically divided into optimization algorithms (where constraint consideration occurs explicitly) and procedural generators (where constraints can only be taken into account implicitly). As investigated in this thesis, these two automation strategies follow two fundamentally different paradigms denoted as top-down automation and bottom-up automation. The major trait of top-down automation is that it requires a thorough formalization of the problem to enable a self-intelligent solution finding, whereas a bottom-up automatism –controlled by parameters– merely reproduces solutions that have been preconceived by a layout expert in advance. Since the strengths of one paradigm may compensate the weaknesses of the other, it is assumed that a combination of both paradigms –called bottom-up meets top-down– has much more potential to tackle the analog design problem in its entirety than either optimization-based or generator-based approaches alone. Against this background, the thesis at hand presents Self-organized Wiring and Arrangement of Responsive Modules (SWARM), an interdisciplinary methodology addressing the design problem with a decentralized multi-agent system. Its basic principle, similar to the roundup of a sheep herd, is to let responsive mobile layout modules (implemented as context-aware procedural generators) interact with each other inside a user-defined layout zone. Each module is allowed to autonomously move, rotate and deform itself, while a supervising control organ successively tightens the layout zone to steer the interaction towards increasingly compact (and constraint compliant) layout arrangements. Considering various principles of self-organization and incorporating ideas from existing decentralized systems, SWARM is able to evoke the phenomenon of emergence: although each module only has a limited viewpoint and selfishly pursues its personal objectives, remarkable overall solutions can emerge on the global scale. Several examples exhibit this emergent behavior in SWARM, and it is particularly interesting that even optimal solutions can arise from the module interaction. Further examples demonstrate SWARM’s suitability for floorplanning purposes and its application to practical place-and-route problems. The latter illustrates how the interacting modules take care of their respective design requirements implicitly (i.e., bottom-up) while simultaneously paying respect to high level constraints (such as the layout outline imposed top-down by the supervising control organ). Experimental results show that SWARM can outperform optimization algorithms and procedural generators both in terms of layout quality and design productivity. From an academic point of view, SWARM’s grand achievement is to tap fertile virgin soil for future works on novel bottom-up meets top-down automatisms. These may one day be the key to close the automation gap in analog layout design.Nach mehr als drei Jahrzehnten Entwurfsautomatisierung werden die meisten Layouts für analoge integrierte Schaltkreise heute immer noch in aufwändiger Handarbeit entworfen. Gegenüber den hochautomatisierten Synthesewerkzeugen im Digitalbereich (die sich mit dem quantitativen Problem auseinandersetzen, mehr und mehr Komponenten auf einem einzelnen Chip unterzubringen – bestens bekannt als More Moore) kämpft die analoge Layoutautomatisierung mit den vielen verschiedenen und stark korrelierten funktionalen Anforderungen, die das analoge Entwurfsproblem zu einer More than Moore Herausforderung machen. Angesichts dieser qualitativen Komplexität bedarf es des umfassenden Expertenwissens erfahrener Layouter um sämtliche Entwurfsconstraints, die zwingend eingehalten werden müssen, zu berücksichtigen. Meist beinhaltet dies formal spezifiziertes als auch nicht-formal übermitteltes Expertenwissen, was eine explizite bzw. implizite Constraint Berücksichtigung nach sich zieht. Existierende Automatisierungsansätze können grundsätzlich unterteilt werden in Optimierungsalgorithmen (wo die Constraint Berücksichtigung explizit erfolgt) und prozedurale Generatoren (die Constraints nur implizit berücksichtigen können). Wie in dieser Arbeit eruiert wird, folgen diese beiden Automatisierungsstrategien zwei grundlegend unterschiedlichen Paradigmen, bezeichnet als top-down Automatisierung und bottom-up Automatisierung. Wesentliches Merkmal der top-down Automatisierung ist die Notwendigkeit einer umfassenden Problemformalisierung um eine eigenintelligente Lösungsfindung zu ermöglichen, während ein bottom-up Automatismus –parametergesteuert– lediglich Lösungen reproduziert, die vorab von einem Layoutexperten vorgedacht wurden. Da die Stärken des einen Paradigmas die Schwächen des anderen ausgleichen können, ist anzunehmen, dass eine Kombination beider Paradigmen –genannt bottom-up meets top down– weitaus mehr Potenzial hat, das analoge Entwurfsproblem in seiner Gesamtheit zu lösen als optimierungsbasierte oder generatorbasierte Ansätze für sich allein. Vor diesem Hintergrund stellt die vorliegende Arbeit Self-organized Wiring and Arrangement of Responsive Modules (SWARM) vor, eine interdisziplinäre Methodik, die das Entwurfsproblem mit einem dezentralisierten Multi-Agenten-System angeht. Das Grundprinzip besteht darin, ähnlich dem Zusammentreiben einer Schafherde, reaktionsfähige mobile Layoutmodule (realisiert als kontextbewusste prozedurale Generatoren) in einer benutzerdefinierten Layoutzone interagieren zu lassen. Jedes Modul darf sich selbständig bewegen, drehen und verformen, wobei ein übergeordnetes Kontrollorgan die Zone schrittweise verkleinert, um die Interaktion auf zunehmend kompakte (und constraintkonforme) Layoutanordnungen hinzulenken. Durch die Berücksichtigung diverser Selbstorganisationsgrundsätze und die Einarbeitung von Ideen bestehender dezentralisierter Systeme ist SWARM in der Lage, das Phänomen der Emergenz hervorzurufen: obwohl jedes Modul nur eine begrenzte Sichtweise hat und egoistisch seine eigenen Ziele verfolgt, können sich auf globaler Ebene bemerkenswerte Gesamtlösungen herausbilden. Mehrere Beispiele veranschaulichen dieses emergente Verhalten in SWARM, wobei besonders interessant ist, dass sogar optimale Lösungen aus der Modulinteraktion entstehen können. Weitere Beispiele demonstrieren SWARMs Eignung zwecks Floorplanning sowie die Anwendung auf praktische Place-and-Route Probleme. Letzteres verdeutlicht, wie die interagierenden Module ihre jeweiligen Entwurfsanforderungen implizit (also: bottom-up) beachten, während sie gleichzeitig High-Level-Constraints berücksichtigen (z.B. die Layoutkontur, die top-down vom übergeordneten Kontrollorgan auferlegt wird). Experimentelle Ergebnisse zeigen, dass Optimierungsalgorithmen und prozedurale Generatoren von SWARM sowohl bezüglich Layoutqualität als auch Entwurfsproduktivität übertroffen werden können. Aus akademischer Sicht besteht SWARMs große Errungenschaft in der Erschließung fruchtbaren Neulands für zukünftige Arbeiten an neuartigen bottom-up meets top-down Automatismen. Diese könnten eines Tages der Schlüssel sein, um die Automatisierungslücke im analogen Layoutentwurf zu schließen

Aeronautical Engineering: A continuing bibliography with indexes (supplement 206)

This bibliography lists 422 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1986