Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Distributed Computing
This book constitutes the proceedings of the 28th International Symposium on Distributed Computing, DISC 2014, held in Austin, TX, USA, in October 2014. The 35 full papers presented in this volume were carefully reviewed and selected from 148 full paper submissions. In the back matter of the volume a total of 18 brief announcements is presented. The papers are organized in topical sections named: concurrency; biological and chemical networks; agreement problems; robot coordination and scheduling; graph distances and routing; radio networks; shared memory; dynamic and social networks; relativistic systems; transactional memory and concurrent data structures; distributed graph algorithms; and communication.
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Distributed Systems and Applications of Information Filtering and Retrieval
This volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the special session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART) of the 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), held in Barcelona, Spain, on 4-7 October 2012.The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining".Contributions have been careful reviewed by experts in the area, who also gave useful suggestions to improve the quality of the volume.
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Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks
This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond.
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Optimization
This book presents a comprehensive treatment of necessary conditions for general optimization problems. The presentation is carried out in the context of a general theory for extremal problems in a topological vector space setting. Following a brief summary of the required background, generalized Lagrange multiplier rules are derived for optimization problems with equality and generalized "inequality" constraints. The treatment stresses the importance of the choice of the underlying set over which the optimization is to be performed, the delicate balance between differentiability-continuity requirements on the constraint functionals, and the manner in which the underlying set is approximated by a convex set. The generalized multiplier rules are used to derive abstract maximum principles for classes of optimization problems defined in terms of operator equations in a Banach space. It is shown that special cases include the usual maximum principles for general optimal control problems described in terms of diverse systems such as ordinary differential equations, functional differential equations, Volterra integral equations, and difference equations.Careful distinction is made throughout the analysis between "local" and "global" maximum principles. Originally published in 1977. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
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Future Directions in Distributed Computing
Every year we witness acceleration in the availability, deployment, and use of distributed applications. However building increasingly sophisticated applications for extant and emerging networked systems continues to be challenging for several reasons: – Abstract models of computation used in distributed systems research often do not fully capture the limitations and the unpredictable nature of realistic distributed computing platforms; – Fault-tolerance and ef?ciency of computation are dif?cult to combine when the c- puting medium is subject to changes, asynchrony, and failures; – Middleware used for constructing distributed software does not provide services most suitable for sophisticated distributed applications; – Middleware services are speci?ed informally and without precise guarantees of e- ciency, fault-tolerance, scalability, and compositionality; – Speci?cation of distributed deployment of software systems is often left out of the development process; – Finally, there persists an organizational and cultural gap between engineering groups developing systems in a commercial enterprise, and research groups advancing the scienti?c state-of-the-art in academic and industrial settings. The objectives of this book are: (1) to serve as a motivation for de?ning future research programs in distributed computing, (2) to help identify areas where practitioners and engineers on the one hand and scientists and researchers on the other can improve the state of distributed computing through synergistic efforts, and (3) to motivate graduate students interested in entering the exciting research ?eld of distributed computing.
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Evolutionary Multi-Criterion Optimization
This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality , the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).
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Systems, Control, Modeling and Optimization
This constitutes the Proceedings of the 22nd IFIP TC7 Conference held in July 2005, in Torino, Italy, and dedicated to Camillo Possio, on the 60th anniversary of his death during the last air raid over Torino. The papers in this volume concern primarily stochastic and distributed systems, their control/optimization, and inverse problems. These proceedings also explore applications of optimization techniques and computational methods in fields such as medicine, biology and economics.
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Deterministic Global Optimization
This monograph deals with a general class of solution approaches in deterministic global optimization, namely the geometric branch-and-bound methods which are popular algorithms, for instance, in Lipschitzian optimization, d.c. programming, and interval analysis.It also introduces a new concept for the rate of convergence and analyzes several bounding operations reported in the literature, from the theoretical as well as from the empirical point of view. Furthermore, extensions of the prototype algorithm for multicriteria global optimization problems as well as mixed combinatorial optimization problems are considered. Numerical examples based on facility location problems support the theory. Applications of geometric branch-and-bound methods, namely the circle detection problem in image processing, the integrated scheduling and location makespan problem, and the median line location problem in the three-dimensional space are also presented.The book is intended for both researchers and students in the areas of mathematics, operations research, engineering, and computer science.
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Achieving Real-Time in Distributed Computing
Achieving Real-Time in Distributed Computing: From Grids to Clouds offers over 400 accounts from a wide range of specific research efforts. Major focus is given to the need for methodologies, tools, and architectures for complex distributed systems that address the practical issues of performance guarantees, timed execution, real-time management of resources, synchronized communication under various load conditions, satisfaction of QoS constraints, as well as dealing with the trade-offs between these aspects.
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Integrated Model of Distributed Systems
In modern distributed systems, such as the Internet of Things or cloud computing, verifying their correctness is an essential aspect. This requires modeling approaches that reflect the natural characteristics of such systems: the locality of their components, autonomy of their decisions, and their asynchronous communication. However, most of the available verifiers are unrealistic because one or more of these features are not reflected. Accordingly, in this book we present an original formalism: the Integrated Distributed Systems Model (IMDS), which defines a system as two sets (states and messages), and a relation of the "actions" between these sets. The server view and the traveling agent’s view of the system provide communication duality, while general temporal formulas for the IMDS allow automatic verification. The features that the model checks include: partial deadlock and partial termination, communication deadlock and resource deadlock. Automatic verification can support the rapid development of distributed systems. Further, on the basis of the IMDS, the Dedan tool for automatic verification of distributed systems has been developed.
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Evolutionary Computation in Combinatorial Optimization
This book constitutes the refereed proceedings of the 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events: EvoMUSART, EvoApplications, and EuroGP. The 14 revised full papers presented in this book were carefully reviewed and selected from 42 submissions. They cover a wide spectrum of topics, ranging from the foundations of evolutionary algorithms and other search heuristics to their accurate design and application to combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of core components of metaheuristics, the clever design of their search principles, and their careful selection and configuration. Applications cover problem domains such as scheduling, routing, search-based software engineering and general graph problems. The range of topics covered in this volume reflects the current state of research in the fields of evolutionary computation and combinatorial optimization.
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Combinatorial Optimization and Applications
The conference proceeding LNCS 11346 constitutes the refereed proceedings of the 12th International Conference on Combinatorial Optimization and Applications, COCOA 2018, held in Atlanta, GA, USA, in December 2018.The 50 full papers presented were carefully reviewed and selected from 106 submissions. The papers cover most aspects of t graph algorithms, routing and network design problems, scheduling algorithms, network optimization, combinatorial algorithms, approximation algorithms, paths and connectivity problems and much more.
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Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science
Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of artificial intelligence (AI), Internet of Things (IoT) and data science for future communications systems.The goal of AI, IoT and data science for future communications systems is to create a venue for industry and academics to collaborate on the development of network and system solutions based on data science, AI and IoT. Recent breakthroughs in IoT, mobile and fixed communications and computation have paved the way for a data‑centric society of the future. New applications are increasingly reliant on machine‑to‑machine connections, resulting in unusual workloads and the need for more efficient and dependable infrastructures. Such a wide range of traffic workloads and applications will necessitate dynamic and highly adaptive network environments capable of self‑optimization for the task at hand while ensuring high dependability and ultra‑low latency.Networking devices, sensors, agents, meters and smart vehicles/systems generate massive amounts of data, necessitating new levels of security, performance and dependability. Such complications necessitate the development of new tools and approaches for providing successful services, management and operation. Predictive network analytics will play a critical role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g., preventing network failures and anticipating capacity requirements) and overall network decision‑making. To increase user experience and service quality, data mining and analytic techniques for inferring quality of experience (QoE) signals are required.AI, IoT, machine learning, reinforcement learning and network data analytics innovations open new possibilities in areas such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi‑agent systems and network ultra‑broadband deployment prioritization. These new analytic platforms will aid in the transformation of our networks and user experience. Future networks will enable unparalleled automation and optimization by intelligently gathering, analyzing, learning and controlling huge volumes of information.
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Intelligent Distributed Video Surveillance Systems
There is a growing interest in the development and deployment of surveillance systems in public and private locations. Conventional approaches rely on the installation of wide area CCTV (Closed Circuit Television), but the explosion in the numbers of cameras that have to be monitored, the increasing costs of providing monitoring personnel and the limitations that humans have to maintain sustained levels of concentration severely limit the effectiveness of these systems. Advances in information and communication technologies, such as computer vision for face recognition and human behaviour analysis, digital annotation and storage of video, transmission of video/audio streams over wired and wireless networks, can potentially provide significant improvements in this field. The book consists of a coherent selection of extended versions of presentations made in two successful IEE symposia on Intelligent Distributed Surveillance Systems (IDSS). It surveys recent development in distributed intelligent surveillance systems and brings together the work of researchers and engineers, system integrators and managers of public and private organisations likely to use such systems.
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Advances in Optimization and Decision Science for Society, Services and Enterprises
The contributions included in the volume are drawn from presentations at ODS2019 – International Conference on Optimization and Decision Science, which was the 49th annual meeting of the Italian Operations Research Society (AIRO) held at Genoa, Italy, on 4-7 September 2019. This book presents very recent results in the field of Optimization and Decision Science. While the book is addressed primarily to the Operations Research (OR) community, the interdisciplinary contents ensure that it will also be of very high interest for scholars and researchers from many scientific disciplines, including computer sciences, economics, mathematics, and engineering. Operations Research is known as the discipline of optimization applied to real-world problems and to complex decision-making fields. The focus is on mathematical and quantitative methods aimed at determining optimal or near-optimal solutions in acceptable computation times. This volume not only presents theoretical results but also covers real industrial applications, making it interesting for practitioners facing decision problems in logistics, manufacturing production, and services. Readers will accordingly find innovative ideas from both a methodological and an applied perspective.
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Analytics and Optimization for Renewable Energy Integration
The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
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VLSI for Artificial Intelligence and Neural Networks
This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.
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Integrated Sensing and Communications for Future Wireless Networks
Integrated Sensing and Communications for Future Wireless Networks: Principles, Advances and Key Enabling Technologies presents the principles, methods, and algorithms of ISAC, an overview of the essential enabling technologies, as well as the latest research and future directions. Suitable for academic researchers and post graduate students as well as industry R&D engineers, this book is the definitive reference in this interdisciplinary field that is being seen as a technology to enable emerging applications such as vehicular networks, environmental monitoring, remote sensing, IoT, smart cities. Importantly, ISAC has been identified as an enabling technology for B5G/6G, and the next-generation Wi-Fi system. ISAC brings together a range of technologies: radar sensing, reconfigurable intelligent surfaces, holographic surfaces through to high frequency terahertz, PHY security, channel signaling, multiple access, and machine learning.
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Advances in Parallel and Distributed Computing and Ubiquitous Services
This book contains the combined proceedings of the 4th International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-15) and the 16th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT-15). The combined proceedings present peer-reviewed contributions from academic and industrial researchers in fields including ubiquitous and context-aware computing, context-awareness reasoning and representation, location awareness services, and architectures, protocols and algorithms, energy, management and control of wireless sensor networks. The book includes the latest research results, practical developments and applications in parallel/distributed architectures, wireless networks and mobile computing, formal methods and programming languages, network routing and communication algorithms, database applications and data mining, access control and authorization and privacy preserving computation.
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Soft Computing Based Optimization and Decision Models
This book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis. Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection. The book is dedicated to Professor José L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.
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Maximizing Business Performance and Efficiency through Intelligent Systems
Technology has vastly advanced over the years and created new developments and uses across various industries. By applying these new approaches in the business world, process management and organization can be significantly improved. Maximizing Business Performance and Efficiency through Intelligent Systems is an essential reference publication for the latest research on methods to use artificial intelligence in organizational settings. Featuring coverage on a broad range of topics such as information retrieval, fuzzy systems, and neural networks, this book is ideally designed for students, professionals, and researchers seeking research on emerging advances in business technology applications.
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Survivability and Traffic Grooming in WDM Optical Networks
The advent of fiber optic transmission systems and wavelength division multiplexing (WDM) have led to a dramatic increase in the usable bandwidth of single fiber systems. This book provides detailed coverage of survivability (dealing with the risk of losing large volumes of traffic data due to a failure of a node or a single fiber span) and traffic grooming (managing the increased complexity of smaller user requests over high capacity data pipes), both of which are key issues in modern optical networks. A framework is developed to deal with these problems in wide-area networks, where the topology used to service various high-bandwidth (but still small in relation to the capacity of the fiber) systems evolves toward making use of a general mesh. Effective solutions, exploiting complex optimization techniques, and heuristic methods are presented to keep network problems tractable. Newer networking technologies and efficient design methodologies are also described.
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Optimization, Parallel Processing and Applications
It is already a tradition that conferences on operations research are organized by the Mathematisches Forschungsinstitut in Oberwolfach/Germany. The mean point of the 1987 conference was to discuss recentl.v developed methods in optimization theory derived from various fields of mathematics. On the other hand, the practical use of results in operations research is very important. In the last few years* essenti.al progress in this direction was made at the International Insti- tute for Applied Systems Analysis (IIASA) at Laxenburg/Austria. Therefore a three days workshop on Advanced Computation Techniques, Parallel Processing and Optimi- zation organized by IIASA and the University of Karlsruhe immediately followed the Oberwolfach Conference. This volume contains selected pape~s which have been presented at one of these conferences. It:is divided into five sections based on the above topics: I. Algorithms and Optimization Methods II. Optimization and Parallel Processing III. Graph Theory and Scheduling IV. Differential Equations and Operator Theory V. Applications. We would like to thank the director of the Mathematisches Forschungsinstitut Oberwolfach Prof. Dr. M.Barner and the International Institute for Applied Systems Analysis, particularly Prof. Dr. V. Kaftanov, and also to the director of the Computer Center of the University of Karlsruhe Prof. Dr. A. Schreiner for their support in organizing these conferences. We also appreciate the excellent coopera- tion of Springer Verlag. We also thank Dr. P. Recht, Dr. D. Solte and Dr. K. Wieder as well as*Mrs.
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Graph Theoretic Approaches for Analyzing Large-Scale Social Networks
Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information.Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.The many academic areas covered in this publication include, but are not limited to:Content Specific ModelingDistributed MemoryGraph MiningInfluence MaximizationInformation Spread ControlLink PredictionProbabilistic Exploration
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Multimodal Optimization by Means of Evolutionary Algorithms
This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
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Multi-objective Optimization Techniques
The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms.Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms.Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems.Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing.Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling.The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.
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Evolutionary Optimization in Dynamic Environments
Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.
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Research Advancements in Smart Technology, Optimization, and Renewable Energy
As environmental issues remain at the forefront of energy research, renewable energy is now an all-important field of study. And as smart technology continues to grow and be refined, its applications broaden and increase in their potential to revolutionize sustainability studies. This potential can only be fully realized with a thorough understanding of the most recent breakthroughs in the field. Research Advancements in Smart Technology, Optimization, and Renewable Energy is a collection of innovative research that explores the recent steps forward for smart applications in sustainability. Featuring coverage on a wide range of topics including energy assessment, neural fuzzy control, and biogeography, this book is ideally designed for advocates, policymakers, engineers, software developers, academicians, researchers, and students.
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Advancing the Next-Generation of Mobile Computing
Advancing the Next-Generation of Mobile Computing: Emerging Technologies offers historical perspectives on mobile computing, as well as new frameworks and methodologies for mobile networks, intelligent mobile applications, and mobile computing applications. This collection of research aims to inform researchers, designers, and users of mobile technology and promote awareness of new trends and tools in this growing field of study.
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Modeling and Optimization of Interdependent Energy Infrastructures
This book opens up new ways to develop mathematical models and optimization methods for interdependent energy infrastructures, ranging from the electricity network, natural gas network, district heating network, and electrified transportation network. The authors provide methods to help analyze, design, and operate the integrated energy system more efficiently and reliably, and constitute a foundational basis for decision support tools for the next-generation energy network. Chapters present new operation models of the coupled energy infrastructure and the application of new methodologies including convex optimization, robust optimization, and equilibrium constrained optimization. Four appendices provide students and researchers with helpful tutorials on advanced optimization methods: Basics of Linear and Conic Programs; Formulation Tricks in Integer Programming; Basics of Robust Optimization; Equilibrium Problems. This book provides theoretical foundation and technical applications for energy system integration, and the the interdisciplinary research presented will be useful to readers in many fields including electrical engineering, civil engineering, and industrial engineering.
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Networks in Action
One of the most well-known of all network optimization problems is the shortest path problem, where a shortest connection between two locations in a road network is to be found. This problem is the basis of route planners in vehicles and on the Internet. Networks are very common structures; they consist primarily of a ?nite number of locations (points, nodes), together with a number of links (edges, arcs, connections) between the locations. Very often a certain number is attached to the links, expressing the distance or the cost between the end points of that connection. Networks occur in an extremely wide range of applications, among them are: road networks; cable networks; human relations networks; project scheduling networks; production networks; distribution networks; neural networks; networks of atoms in molecules. In all these cases there are “objects” and “relations” between the objects. A n- work optimization problem is actually nothing else than the problem of ?nding a subset of the objects and the relations, such that a certain optimization objective is satis?ed.
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Self-Managing Distributed Systems
This book constitutes the refereed proceedings of the 14th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, DSOM 2003, held in Heidelberg, Germany in October 2002.The 20 revised full papers and 6 revised short papers presented together with a keynote paper were carefully reviewed and selected from a total of 105 submissions. The papers are organized in topical sections on self-configuration, peer-to-peer management, self-optimization and performance management, utility management, self-protection and access control, manageability and instrumentation, and context-awareness.
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Recent Advances of Hybrid Intelligent Systems Based on Soft Computing
This book describes recent advances on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book contains a collection of papers focused on hybrid intelligent systems based on soft computing. There are some papers with the main theme of type-1 and type-2 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications. There are also some papers that present theory and practice of meta-heuristics in different areas of application. Another group of papers describes diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical applications. There are also some papers that present theory and practice of neural networks in different areas of application. In addition, there are papers that present theory and practice of optimization and evolutionary algorithms in different areas of application. Finally, there are some papers describing applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems.
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Distributed Consensus in Multi-vehicle Cooperative Control
Assuming only neighbor-neighbor interaction among vehicles, this monograph develops distributed consensus strategies that ensure that the information states of all vehicles in a network converge to a common value. Readers learn to deal with groups of autonomous vehicles in aerial, terrestrial, and submarine environments. Plus, they get the tools needed to overcome impaired communication by using constantly updated neighbor-neighbor interchange.
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Distributed Strategic Learning for Wireless Engineers
Although valued for its ability to allow teams to collaborate and foster coalitional behaviors among the participants, game theory’s application to networking systems is not without challenges. Distributed Strategic Learning for Wireless Engineers illuminates the promise of learning in dynamic games as a tool for analyzing network evolution and underlines the potential pitfalls and difficulties likely to be encountered.Establishing the link between several theories, this book demonstrates what is needed to learn strategic interaction in wireless networks under uncertainty, randomness, and time delays. It addresses questions such as: How much information is enough for effective distributed decision making?Is having more information always useful in terms of system performance?What are the individual learning performance bounds under outdated and imperfect measurement?What are the possible dynamics and outcomes if the players adopt different learning patterns?If convergence occurs, what is the convergence time of heterogeneous learning?What are the issues of hybrid learning?How can one develop fast and efficient learning schemes in scenarios where some players have more information than the others?What is the impact of risk-sensitivity in strategic learning systems?How can one construct learning schemes in a dynamic environment in which one of the players do not observe a numerical value of its own-payoffs but only a signal of it?How can one learn "unstable" equilibria and global optima in a fully distributed manner?The book provides an explicit description of how players attempt to learn over time about the game and about the behavior of others. It focuses on finite and infinite systems, where the interplay among the individual adjustments undertaken by the different players generates different learning dynamics, heterogeneous learning, risk-sensitive learning, and hybrid dynamics.
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Sequential Approximate Multiobjective Optimization Using Computational Intelligence
Many kinds of practical problems such as engineering design, industrial m- agement and ?nancial investment have multiple objectives con?icting with eachother. Thoseproblemscanbeformulatedasmultiobjectiveoptimization. In multiobjective optimization, there does not necessarily a unique solution which minimizes (or maximizes) all objective functions. We usually face to the situation in which if we want to improve some of objectives, we have to give up other objectives. Finally, we pay much attention on how much to improve some of objectives and instead how much to give up others. This is called “trade-o?. ” Note that making trade-o? is a problem of value ju- ment of decision makers. One of main themes of multiobjective optimization is how to incorporate value judgment of decision makers into decision s- port systems. There are two major issues in value judgment (1) multiplicity of value judgment and (2) dynamics of value judgment. The multiplicity of value judgment is treated as trade-o? analysis in multiobjective optimi- tion. On the other hand, dynamics of value judgment is di?cult to treat. However, it is natural that decision makers change their value judgment even in decision making process, because they obtain new information during the process. Therefore, decision support systems are to be robust against the change of value judgment of decision makers. To this aim, interactive p- grammingmethodswhichsearchasolutionwhileelicitingpartialinformation on value judgment of decision makers have been developed. Those methods are required to perform ?exibly for decision makers’ attitude.
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Distributed Computer-Aided Engineering
Networking of personal computers and workstations is becoming commonplace in academic and industrial environments. A cluster of workstations provides engineers with a familiar, cost-effective environment for high performance computing. However, workstations often have no dedicated link and communicate slowly on a local area network (LAN), such as the Ethernet. Thus, to effectively harness the parallel processing or distributed computing capabilities of workstations, new algorithms need to be developed with a higher computation-to-communication ratio.Distributed Computer-Aided Engineering presents distributed algorithms for three fundamental areas: finite element analysis, design optimization, and visualization - providing a new direction in high performance structural engineering computing.
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
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R 1,069.00
Optimization
Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students’ skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications.In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.
R 6,124.24
Unconscious Networks
This book develops an original theoretical framework for understanding human-technology relations. The author’s approach, which he calls technoanalysis, analyzes artificial intelligence based on Freudian psychoanalysis, biosemiotics, and Latour’s actor-network theory.How can we communicate with AI to determine shared values and objectives? And what, ultimately, do we want from machines? These are crucial questions in our world, where the influence of AI-based technologies is rapidly growing. Unconscious dynamics influence AI and digital technology and understanding them is essential to better controlling AI systems. This book’s unique methodology— which combines psychoanalysis, biosemiotics, and actor-network theory—reveals a radical reformulation of the problem of the human mind. Technoanalysis views the mind as a hybrid network of humans and nonhuman actants in constant interaction with one another. The author argues that human unconscious dynamics influence and shape technology, just as technology influences and shapes human unconscious dynamics. He proceeds to show how this conception of the relationship between the unconscious and technology can be applied to social robotics and AI.Unconscious Networks will appeal to scholars and advanced students interested in philosophy of technology, philosophy of artificial intelligence, psychoanalysis, and science and technology studies.
R 2,481.53
Modelling and Intelligent Optimisation of Production Scheduling in VCIM Systems
This thesis reports on an innovative production-scheduling model for virtual computer-integrated manufacturing (VCIM) systems. It also describes a robust genetic algorithm for production scheduling in VCIM systems. The model, which is the most comprehensive of its kind to date, is not only capable of supporting collaborative shipment scheduling and handling multiple product orders simultaneously, but also helps cope with multiple objective functions under uncertainties. In turn, the genetic algorithm, characterised by an innovative algorithm structure, chromosome encoding, crossover and mutation, is capable of searching for optimal/suboptimal solutions to the complex optimisation problem in the VCIM production- scheduling model described. Lastly, the effectiveness of the proposed approach is verified in a comprehensive case study.
R 3,563.45
Interdisciplinary Approaches to Spatial Optimization Issues
As metropolises continue to see a growth in population, planners are continually searching for trending methods for utilizing space and seeking the best geographical arrangements for these cities. Professionals have continually used geographic information systems (GIS) to solve these issues; however, limitations in this technology remain prevalent. Integrating multiple-criteria decision analysis and evolutionary computing tools with GIS has created an array of robust solutions for spatial optimization problems in densely populated areas.Interdisciplinary Approaches to Spatial Optimization Issues is a pivotal reference source that provides vital research on advancements within the field of GIS and evolutionary solutions for spatial optimization issues. While highlighting topics such as computing machinery, vehicular routing, and operational research, this publication is ideally designed for practitioners, technicians, developers, academicians, students, government officials, planners, and researchers seeking current research on applications and improvements within spatial optimization and GIS.
R 8,314.31
Wireless Sensor Networks
Wireless Sensor Networks: Evolutionary Algorithms for Optimizing Performance provides an integrative overview of bio-inspired algorithms and their applications in the area of Wireless Sensor Networks (WSN). Along with the usage of the WSN, the number of risks and challenges occurs while deploying any WSN. Therefore, to defeat these challenges some of the bio-inspired algorithms are applied and discussed in this book. Discussion includes a broad, integrated perspective on various challenges and issues in WSN and also impact of bio-inspired algorithms on the lifetime of the WSN. It creates interdisciplinary theory, concepts, definitions, models and findings involved in WSN and Bio-inspired algorithms making it an essential guide and reference. It includes various WSN examples making the book accessible to a broader interdisciplinary readership.The book offers comprehensive coverage of the most essential topics, including: Evolutionary algorithms Swarm intelligence Hybrid algorithms Energy efficiency in WSN Load balancing of gateways Localization Clustering and routing Designing fitness functions according to the issues in WSN.The book explains about practices of shuffled complex evolution algorithm, shuffled frog leaping algorithm, particle swarm optimization and dolphin swarm optimization to defeat various challenges in WSN. The author elucidates how we must transform our thinking, illuminating the benefits and opportunities offered by bio-inspired approaches to innovation and learning in the area of WSN. This book serves as a reference book for scientific investigators who shows an interest in evolutionary computation and swarm intelligence as well as issues and challenges in WSN.
R 6,647.69
