Search results for "Advancing Intelligent Networks Through Distributed Optimization"
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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
This edited book presents scientific results of 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2014) held on June 30 – July 2, 2014 in Las Vegas Nevada, USA. The aim of this conference was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas, research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected the 13 outstanding papers from those papers accepted for presentation at the conference.
R 3,023.23
Multi-Objective Optimization of Industrial Power Generation Systems
The increased complexity of the economy in recent years has led to the advancement of energy generation systems. Engineers in this industrial sector have been compelled to seek contemporary methods to keep pace with the rapid development of these systems. Computational intelligence has risen as a capable method that can effectively resolve complex scenarios within the power generation sector. In-depth research on the various applications of this technology is lacking, as engineering professionals need up-to-date information on how to successfully utilize computational intelligence in industrial systems.Multi-Objective Optimization of Industrial Power Generation Systems: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of the application of intelligent optimization techniques within industrial energy systems. Featuring coverage on a broad range of topics such as swarm intelligence, renewable energy, and predictive modeling, this book is ideally designed for industrialists, engineers, industry professionals, researchers, students, and academics seeking current research on computational intelligence frameworks within the power generation sector.
R 8,302.95
Intelligent Systems and Networks
This book presents Proceedings of the International Conference on Intelligent Systems and Networks (ICISN 2021), held at Hanoi in Vietnam. It includes peer-reviewed high-quality articles on intelligent system and networks. It brings together professionals and researchers in the area and presents a platform for exchange of ideas and to foster future collaboration. The topics covered in this book include—foundations of computer science; computational intelligence language and speech processing; software engineering software development methods; wireless communications signal processing for communications; electronics track IoT and sensor systems embedded systems; etc.
R 7,585.03
Ant Colony Optimization and Swarm Intelligence
1 With its fourth edition, the ANTS series of workshops has changed its name. Theoriginal"ANTS-FromAntColoniestoArti?cialAnts:InternationalWo- shop on Ant Algorithms" has become "ANTS - International Workshop on Ant Colony Optimization and Swarm Intelligence". This change is mainly due to the following reasons. First, the term "ant algorithms" was slower in spreading in the research community than the term "swarm intelligence", while at the sametime research inso-calledswarm robotics wasthesubjectofincreasingactivity:itwastherefore an obvious choice to substitute the term ant algorithms with the more accepted and used term swarm intelligence. Second, although swarm intelligence research has undoubtedly produced a 2 number of interesting and promising research directions , we think it is fair to say that its most successful strand is the one known as "ant colony optimi- tion".Ant colony optimization, ?rst introducedin the early1990sasa noveltool fortheapproximatesolutionofdiscreteoptimizationproblems,hasrecentlyseen an explosion in the number of its applications, both to academic and real-world problems, and is currently being extended to the realm of continuous optimi- tion (a few papers on this subject being published in these proceedings).It is therefore a reasonable choice to have the term ant colony optimization as part of the workshop name.
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Computational Intelligence in Sensor Networks
This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.
R 7,326.00
Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
R 1,318.87
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
This editedbook presents scientific results of the 17th IEEE/ACIS InternationalConference on Software Engineering, Artificial Intelligence, Networking andParallel/Distributed Computing (SNPD 2016) which was held on May 30 - June 1,2016 in Shanghai, China. The aim of this conference was to bringtogether researchers and scientists, businessmen and entrepreneurs, teachers,engineers, computer users, and students to discuss the numerous fields ofcomputer science and to share their experiences and exchange new ideas andinformation in a meaningful way. Research results about all aspects (theory,applications and tools) of computer and information science, and to discuss thepractical challenges encountered along the way and the solutions adopted tosolve them.
R 4,679.44
Modern Optimization Techniques for Smart Grids
Modern Optimization Techniques for Smart Grids presents current research and methods for monitoring transmission systems and enhancing distribution system performance using optimization techniques considering the role of different single and multi-objective functions. The authors present in-depth information on integrated systems for smart transmission and distribution, including using smart meters such as phasor measurement units (PMUs), enhancing distribution system performance using the optimal placement of distributed generations (DGs) and/or capacitor banks, and optimal capacitor placement for power loss reduction and voltage profile improvement. The book will be a valuable reference for researchers, students, and engineers working in electrical power engineering and renewable energy systems.Predicts future development of hybrid power systems;Introduces enhanced optimization strategies;Includes MATLAB M-file codes.
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Multi-Objective Optimization using Evolutionary Algorithms
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive coverage of this growing area of researchCarefully introduces each algorithm with examples and in-depth discussionIncludes many applications to real-world problems, including engineering design and schedulingIncludes discussion of advanced topics and future researchCan be used as a course text or for self-studyAccessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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Computational Intelligence for Unmanned Aerial Vehicles Communication Networks
This book aims to provide a vision that can combine the best of both Artificial Intelligence (AI) and communication networks for designing the deployment trajectory to establish flexible Unmanned Aerial Vehicles (UAV) communication networks.This book will discuss the major challenges that can face deploying unmanned aerial vehicles in emergent networks. It will focus on possible applications of UAV in a Smart City environment where they can be supported by Internet of Things (IoT), wireless sensor networks, as well as 5G, and beyond. This book presents the possible problems and solutions, the network integration of the UAV and compare the communication technologies to be used.This book will be a collection of original contributions regarding state of the art AI/ML based solutions in UAV communication networks which can be used for routing protocol design, transport layer optimization, user/application behaviour prediction, communication network optimization, security, and anomaly detection.
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Distributed Sensor Networks
The best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for students or as research material for engineers, the book gives readers up-to-date, practical insight on all aspects of the field.Revised and expanded, this second edition incorporates contributions from many veterans of the DARPA ISO SENSIT program as well as new material from distinguished researchers in the field. Sensor Networking and Applications focuses on sensor deployment and networking, adaptive tasking, self-configuration, and system control. In the expanded applications section, the book draws on the insight of practitioners in the field.Readers of this book may also be interested in Distributed Sensor Networks, Second Edition: Image and Sensor Signal Processing (ISBN: 9781439862827).
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Optimization of Computer Networks
This book covers the design and optimization of computer networks applying a rigorous optimization methodology, applicable to any network technology. It is organized into two parts. In Part 1 the reader will learn how to model network problems appearing in computer networks as optimization programs, and use optimization theory to give insights on them. Four problem types are addressed systematically – traffic routing, capacity dimensioning, congestion control and topology design. Part 2 targets the design of algorithms that solve network problems like the ones modeled in Part 1. Two main approaches are addressed – gradient-like algorithms inspiring distributed network protocols that dynamically adapt to the network, or cross-layer schemes that coordinate the cooperation among protocols; and those focusing on the design of heuristic algorithms for long term static network design and planning problems. Following a hands-on approach, the reader will have access to a large set of examples in real-life technologies like IP, wireless and optical networks. Implementations of models and algorithms will be available in the open-source Net2Plan tool from which the user will be able to see how the lessons learned take real form in algorithms, and reuse or execute them to obtain numerical solutions. An accompanying link to the author’s own Net2plan software enables readers to produce numerical solutions to a multitude of real-life problems in computer networks (www.net2plan.com).
R 3,353.00
Advances in Intelligent Computing
This edited volume on computational intelligence algorithms-based applications includes work presented at the International Conference on Computational Intelligence, Communications, and Business Analytics (CICBA 2017). It provides the latest research findings on the significance of computational intelligence and related application areas. It also introduces various computation platforms involving evolutionary algorithms, fuzzy logic, swarm intelligence, artificial neural networks and several other tools for solving real-world problems. It also discusses various tools that are hybrids of more than one solution framework, highlighting the theoretical aspects as well as various real-world applications.
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Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Recent advances in gene sequencing technology are now shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any given organism. In order to mediate internal and external signals, the daunting task of classifying an organism's genes into complex signaling pathways needs to be completed. The Handbook of Research on Computational Methodologies in Gene Regulatory Networks focuses on methods widely used in modeling gene networks including structure discovery, learning, and optimization. This innovative Handbook of Research presents a complete overview of computational intelligence approaches for learning and optimization and how they can be used in gene regulatory networks.
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Recent Advances in Computational Optimization
This book presents new optimization approaches and methods and their application in real-world and industrial problems, and demonstrates how many of the problems arising in engineering, economics and other domains can be formulated as optimization problems. Constituting a comprehensive collection of extended contributions from the 9th International Workshop on Computational Optimization (WCO) held in Gdansk, Poland, September 11–14, 2016, the book discusses important applications such as job scheduling, wildfire modeling, parameter settings for controlling different processes, capital budgeting, data mining, finding the location of sensors in a given network, identifying the conformation of molecules, algorithm correctness, decision support system, and computer memory management. Further, it shows how to develop algorithms for these based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming.The book is a valuable resource for researchers and practitioners alike.
R 3,554.20
Network Models and Optimization
Network models are critical tools in business, management, science and industry. “Network Models and Optimization” presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.
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Optimization in Engineering Sciences
The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.
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Non-centralized Optimization-Based Control Schemes for Large-Scale Energy Systems
This book describes the development of innovative non-centralized optimization-based control schemes to solve economic dispatch problems of large-scale energy systems. Particularly, it focuses on communication and cooperation processes of local controllers, which are integral parts of such schemes. The economic dispatch problem, which is formulated as a convex optimization problem with edge‐based coupling constraints, is solved by using methodologies in distributed optimization over time-varying networks, together with distributed model predictive control, and system partitioning techniques. At first, the book describes two distributed optimization methods, which are iterative and require the local controllers to exchange information with each other at each iteration. In turn, it shows that the sequence produced by these methods converges to an optimal solution when some conditions, which include how the controllers must communicate and cooperate, are satisfied. Further, it proposes an information exchange protocol to cope with possible communication link failures. Finally, the proposed distributed optimization methods are extended to the cases with random communication networks and asynchronous updates. Overall, this book presents a set of improved predictive control and distributed optimization methods, together with a rigorous mathematical analysis of each proposed algorithms. It describes a comprehensive approach to cope with communication and cooperation issues of non-centralized control schemes and show how the improved schemes can be successfully applied to solve the economic dispatch problems of large-scale energy systems.
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2nd International Conference on Wireless Intelligent and Distributed Environment for Communication
This book presents the proceedings of the International Conference on Wireless Intelligent and Distributed Environment for Communication (WIDECOM 2019), sponsored by the University of Milan, Milan, Italy, February 11-13, 2019. The conference deals both with the important core and the specialized issues in the areas of new dependability paradigms design and performance of dependable network computing and mobile systems, as well as issues related to the security of these systems. The WIDECOM proceedings features papers addressing issues related to the design, analysis, and implementation, of infrastructures, systems, architectures, algorithms, and protocols that deal with network computing, mobile/ubiquitous systems, cloud systems, and IoT systems. It is a valuable reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners. The book’s structure and content is organized in such a manner that makes it useful at a variety of learning levels.Presents the proceedings of the International Conference on Wireless Intelligent and Distributed Environment for Communication (WIDECOM 2019), Milan, Italy, February 11-13, 2019;Includes an array of topics networking computing, mobile/ubiquitous systems, cloud systems, and IoT systems;Addresses issues related to protecting information security and establishing trust in the digital space.
R 5,248.13
Mobile Networks and Management
This book constitutes the refereed post-conference proceedings of the 12th EAI International Conference on Mobile Networks and Management, MONAMI 2022, which took place virtually during October 29-31, 2022.The 31 full papers were carefully reviewed and selected from 78 submissions. The papers are divided into groups of content as follows: Innovative Artificial Intelligence Applications for Smart City; The New Era of Computer Network by using Machine Learning; Advanced Technologies in Edge and Fog Computing; Emerging Technologies in Mobile Networks and Management; and Recent Advances in Communications and Computing.
R 3,915.97
Recent Advances in Global Optimization
This book will present the papers delivered at the first U.S. conference devoted exclusively to global optimization and will thus provide valuable insights into the significant research on the topic that has been emerging during recent years. Held at Princeton University in May 1991, the conference brought together an interdisciplinary group of the most active developers of algorithms for global optimization in order to focus the attention of the mathematical programming community on the unsolved problems and diverse applications of this field. The main subjects addressed at the conference were advances in deterministic and stochastic methods for global optimization, parallel algorithms for global optimization problems, and applications of global optimization. Although global optimization is primarily a mathematical problem, it is relevant to several other disciplines, including computer science, applied mathematics, physical chemistry, molecular biology, statistics, physics, engineering, operations research, communication theory, and economics. Global optimization problems originate from a wide variety of mathematical models of real-world systems.Some of its applications are allocation and location problems and VLSI and data-base design problems. Originally published in 1991. 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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Applied Artificial Higher Order Neural Networks for Control and Recognition
In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless.Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.
R 7,876.00
Linear Programming and Algorithms for Communication Networks
Explaining how to apply to mathematical programming to network design and control, Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management fills the gap between mathematical programming theory and its implementation in communication networks. From the basics all the way through to more advanced concepts, its comprehensive coverage provides readers with a solid foundation in mathematical programming for communication networks. Addressing optimization problems for communication networks, including the shortest path problem, max flow problem, and minimum-cost flow problem, the book covers the fundamentals of linear programming and integer linear programming required to address a wide range of problems. It also: Examines several problems on finding disjoint paths for reliable communicationsAddresses optimization problems in optical wavelength-routed networksDescribes several routing strategies for maximizing network utilization for various traffic-demand modelsConsiders routing problems in Internet Protocol (IP) networksPresents mathematical puzzles that can be tackled by integer linear programming (ILP)Using the GNU Linear Programming Kit (GLPK) package, which is designed for solving linear programming and mixed integer programming problems, it explains typical problems and provides solutions for communication networks. The book provides algorithms for these problems as well as helpful examples with demonstrations. Once you gain an understanding of how to solve LP problems for communication networks using the GLPK descriptions in this book, you will also be able to easily apply your knowledge to other solvers.
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Stochastic Recursive Algorithms for Optimization
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
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Integrated Methods for Optimization
The first edition of Integrated Methods for Optimization was published in January 2007. Because the book covers a rapidly developing field, the time is right for a second edition. The book provides a unified treatment of optimization methods. It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume. There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together. (1) There is much in common among them intellectually, and to a large degree they can be understood as special cases of a single underlying solution technology. (2) A growing literature reports how they can be profitably integrated to formulate and solve a wide range of problems. (3) Several software packages now incorporate techniques from two or more of these fields.The book provides a unique resource for graduate students and practitioners who want a well-rounded background in optimization methods within a single course of study. Engineering students are a particularly large potential audience, because engineering optimization problems often benefit from a combined approach—particularly where design, scheduling, or logistics are involved. The text is also of value to those studying operations research, because their educational programs rarely cover CP, and to those studying computer science and artificial intelligence (AI), because their curricula typically omit MP and GO. The text is also useful for practitioners in any of these areas who want to learn about another, because it provides a more concise and accessible treatment than other texts.The book can cover so wide a range of material because it focuses on ideas that arerelevant to the methods used in general-purpose optimization and constraint solvers. The book focuses on ideas behind the methods that have proved useful in general-purpose optimization and constraint solvers, as well as integrated solvers of the present and foreseeable future. The second edition updates results in this area and includes several major new topics:Background material in linear, nonlinear, and dynamic programming.Network flow theory, due to its importance in filtering algorithms.A chapter on generalized duality theory that more explicitly develops a unifying primal-dual algorithmic structure for optimization methods.An extensive survey of search methods from both MP and AI, using the primal-dual framework as an organizing principle.Coverage of several additional global constraints used in CP solvers.The book continues to focus on exact as opposed to heuristic methods. It is possible to bring heuristic methods into the unifying scheme described in the book, and the new edition will retain the brief discussion of how this might be done.
R 2,230.90
Applied Optimization Methods for Wireless Networks
Written in a unique style, this book is a valuable resource for faculty, graduate students, and researchers in the communications and networking area whose work interfaces with optimization. It teaches you how various optimization methods can be applied to solve complex problems in wireless networks. Each chapter reviews a specific optimization method and then demonstrates how to apply the theory in practice through a detailed case study taken from state-of-the-art research. You will learn various tips and step-by-step instructions for developing optimization models, reformulations, and transformations, particularly in the context of cross-layer optimization problems in wireless networks involving flow routing (network layer), scheduling (link layer), and power control (physical layer). Throughout, a combination of techniques from both operations research and computer science disciplines provides a holistic treatment of optimization methods and their applications. Each chapter includes homework exercises, with PowerPoint slides and a solutions manual for instructors available online.
R 2,852.71
Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials
The text comprehensively discusses computational models including artificial neural networks, agent-based models, and decision field theory for reliability engineering. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, computer engineering, and materials science. Discusses the development of sustainable materials using metaheuristic approaches. Covers computational models such as agent-based models, ontology, and decision field theory for reliability engineering. Presents swarm intelligence methods such as ant colony optimization, particle swarm optimization, and grey wolf optimization for solving the manufacturing process. Include case studies for industrial optimizations. Explores the use of computational optimization for reliability and maintainability theory. The text covers swarm intelligence techniques including ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms for solving complex industrial problems of the manufacturing industry as well as predicting reliability, maintainability, and availability of several industrial components.
R 7,566.46
Optimization and Data Science in Industrial Engineering
This two-volume set CCIS 2204 and 2205 constitutes the refereed proceedings of the First International Conference on Optimization and Data Science in Industrial Engineering, ODSIE 2023, held in Istanbul, Turkey, during November 16–17, 2023.The 33 full papers and 2 short papers presented in these proceedings were carefully reviewed and selected from 311 submissions.The papers were organized in the following topical sections:Part I: smart and intelligent transportation systems; machine/deep/reinforcement learning in industries; and advances of artificial intelligence/operational research tools in healthcare.Part II: technology, learning and analytics in intelligent systems; expert systems, decision analysis, and advanced optimization; digital transformation of supply chain and logistics systems.
R 3,127.55
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities for the automated tuning of these parameters.
R 3,022.48
Distributed Algorithms
This book constitutes the refereed proceedings of the 11th International Workshop on Distributed Algorithms, WDAG '97, held in Saarbrücken, Germany, in September 1997.The volume presents 20 revised full papers selected from 59 submissions. Also included are three invited papers by leading researchers. The papers address a variety of current issues in the area of distributed algorithms and, more generally, distributed systems such as various particular algorithms, randomized computing, routing, networking, load balancing, scheduling, message-passing, shared-memory systems, communication, graph algorithms, etc.
R 1,564.46
Conductor: Distributed Adaptation for Heterogeneous Networks
Internet heterogeneity is driving a new challenge in application development: adaptive software. Together with the increased Internet capacity and new access technologies, network congestion and the use of older technologies, wireless access, and peer-to-peer networking are increasing the heterogeneity of the Internet. Applications should provide gracefully degraded levels of service when network conditions are poor, and enhanced services when network conditions exceed expectations. Existing adaptive technologies, which are primarily end-to-end or proxy-based and often focus on a single deficient link, can perform poorly in heterogeneous networks. Instead, heterogeneous networks frequently require multiple, coordinated, and distributed remedial actions. Conductor: Distributed Adaptation for Heterogeneous Networks describes a new approach to graceful degradation in the face of network heterogeneity - distributed adaptation - in which adaptive code is deployed at multiple points within a network. The feasibility of this approach is demonstrated by conductor, a middleware framework that enables distributed adaptation of connection-oriented, application-level protocols. By adapting protocols, conductor provides application-transparent adaptation, supporting both existing applications and applications designed with adaptation in mind. Conductor: Distributed Adaptation for Heterogeneous Networks introduces new techniques that enable distributed adaptation, making it automatic, reliable, and secure. In particular, we introduce the notion of semantic segmentation, which maintains exactly-once delivery of the semantic elements of a data stream while allowing the stream to be arbitrarily adapted in transit. We also introduce a secure architecture for automatic adaptor selection, protecting user data from unauthorized adaptation. These techniques are described both in the context of conductor and in the broader context of distributed systems. Finally, this book presents empirical evidence from several case studies indicating that distributed adaptation can allow applications to degrade gracefully in heterogeneous networks, providing a higher quality of service to users than other adaptive techniques. Further, experimental results indicate that the proposed techniques can be employed without excessive cost. Thus, distributed adaptation is both practical and beneficial. Conductor: Distributed Adaptation for Heterogeneous Networks is designed to meet the needs of a professional audience composed of researchers and practitioners in industry and graduate-level students in computer science.
R 3,030.56
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
This editedbook presents scientific results of the 17th IEEE/ACIS InternationalConference on Software Engineering, Artificial Intelligence, Networking andParallel/Distributed Computing (SNPD 2016) which was held on May 30 - June 1,2016 in Shanghai, China. The aim of this conference was to bringtogether researchers and scientists, businessmen and entrepreneurs, teachers,engineers, computer users, and students to discuss the numerous fields ofcomputer science and to share their experiences and exchange new ideas andinformation in a meaningful way. Research results about all aspects (theory,applications and tools) of computer and information science, and to discuss thepractical challenges encountered along the way and the solutions adopted tosolve them.
R 4,030.00
Deep Learning and Its Applications for Vehicle Networks
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
R 2,757.19
Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications
This book describes the latest advances in fuzzy logic, neural networks, and optimization algorithms, as well as their hybrid intelligent combinations, and their applications in the areas such as intelligent control, robotics, pattern recognition, medical diagnosis, time series prediction, and optimization. The topic is highly relevant as most current intelligent systems and devices use some form of intelligent feature to enhance their performance. The book also presents new and advanced models and algorithms of type-2 fuzzy logic and intuitionistic fuzzy systems, which are of great interest to researchers in these areas. Further, it proposes novel, nature-inspired optimization algorithms and innovative neural models. Featuring contributions on theoretical aspects as well as applications, the book appeals to a wide audience.
R 9,521.81
Data-Driven Optimization of Manufacturing Processes
All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
R 9,655.19
Providing Integrity, Awareness, and Consciousness in Distributed Dynamic Systems
The ideas of this book originate from the mobile WAVE approach which allowed us, more than half century ago, to implement citywide heterogeneous computer networks and solve distributed problems on them well before the internet. The invented paradigm evolved into Spatial Grasp Technology resulted in European Patent and eight books which were oriented on concrete applications in graph and network theory, defence and social systems, crises management, simulation of global viruses, gestalt theory, collective robotics, space research, and others. The obtained solutions often exhibited high system qualities like global integrity, distributed awareness, and even consciousness. The current book takes these important characteristics as primary research objectives, together with the theory of patterns covering them all. It starts from reviewing and classification of numerous publications in these areas and then expresses their main features in Spatial Grasp Language, the basic element of the technology. The obtained results can be used for the creation of most advanced civil, defence, and security systems which can effectively operate even in very complex and critical situations. The book also extends applications of the developed spatial grasp paradigm to such areas as psychology and international relations. This could help develop advanced models of world economic, cultural, political, security and military organizations, using for their simulation advanced computer, communication, and information systems and networks. The proper modification of such models, which can cooperate and compete with each other, along with their repeated simulation, may lead to real strategies supporting world peace and prosperity. The current book is oriented on system scientists, application programmers, industry managers, defence and security commanders, university students, especially those interested in advanced MSc and PhD projects on distributed system management, also philosophers, psychologists, United Nations personnel too.
R 7,561.47
Supply Chain Optimization, Design, and Management
Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.
R 6,989.56
Distributed Autonomous Robotic Systems 8
The International Symposia on Distributed Autonomous Robotic Systems (DARS) started at Riken, Japan in 1992. Since then, the DARS symposia have been held every two years: in 1994 and 1996 in Japan (Riken, Wako), in 1998 in Germany (Karlsruhe), in 2000 in the USA (Knoxville, TN), in 2002 in Japan (Fukuoka), in 2004 in France (Toulouse), and in 2006 in the USA (Minneapolis, MN). The 9th DARS symposium, which was held during November 17–19 in T- kuba, Japan, hosted 84 participants from 13 countries. The 48 papers presented there were selected through rigorous peer review with a 50% acceptance ratio. Along with three invited talks, they addressed the spreading research fields of DARS, which are classifiable along two streams: theoretical and standard studies of DARS, and interdisciplinary studies using DARS concepts. The former stream includes multi-robot cooperation (task assignment methodology among multiple robots, multi-robot localization, etc.), swarm intelligence, and modular robots. The latter includes distributed sensing, mobiligence, ambient intelligence, and mul- agent systems interaction with human beings. This book not only offers readers the latest research results related to DARS from theoretical studies to application-oriented ones; it also describes the present trends of this field. With the diversity and depth revealed herein, we expect that DARS technologies will flourish soon.
R 6,048.88
Distributed Computing
This book constitutes the proceedings of the 27th International Symposium on Distributed Computing, DISC 2013, held in Jerusalem, Israel, in October 2013. The 27 full papers presented in this volume were carefully reviewed and selected from 142 submissions; 16 brief announcements are also included. The papers are organized in topical sections named: graph distributed algorithms; topology, leader election, and spanning trees; software transactional memory; shared memory executions; shared memory and storage; gossip and rumor; shared memory tasks and data structures; routing; radio networks and the SINR model; crypto, trust, and influence; and networking.
R 2,223.83
Large-scale Optimization
Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms. Audience: This book is addressed to specialists in operations research, optimization, and optimal control.
R 4,658.11
Evolutionary Optimization Algorithms
A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementationGives a careful treatment of recently developed EAsincluding opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAsIncludes chapter-end problems plus a solutions manual available online for instructorsOffers simple examples that provide the reader with an intuitive understanding of the theoryFeatures source code for the examples available on the author's websiteProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
R 4,655.00
Fractional Order Darwinian Particle Swarm Optimization
This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science.
R 2,015.00
R 763.00
Combinatorial Optimization
A complete, highly accessible introduction to one of today's most exciting areas of applied mathematics One of the youngest, most vital areas of applied mathematics, combinatorial optimization integrates techniques from combinatorics, linear programming, and the theory of algorithms. Because of its success in solving difficult problems in areas from telecommunications to VLSI, from product distribution to airline crew scheduling, the field has seen a ground swell of activity over the past decade. Combinatorial Optimization is an ideal introduction to this mathematical discipline for advanced undergraduates and graduate students of discrete mathematics, computer science, and operations research. Written by a team of recognized experts, the text offers a thorough, highly accessible treatment of both classical concepts and recent results. The topics include: * Network flow problems * Optimal matching * Integrality of polyhedra * Matroids * NP-completeness Featuring logical and consistent exposition, clear explanations of basic and advanced concepts, many real-world examples, and helpful, skill-building exercises, Combinatorial Optimization is certain to become the standard text in the field for many years to come.
R 6,934.00
Neural Networks in Optimization
People are facing more and more NP-complete or NP-hard problems of a combinatorial nature and of a continuous nature in economic, military and management practice. There are two ways in which one can enhance the efficiency of searching for the solutions of these problems. The first is to improve the speed and memory capacity of hardware. We all have witnessed the computer industry's amazing achievements with hardware and software developments over the last twenty years. On one hand many computers, bought only a few years ago, are being sent to elementary schools for children to learn the ABC's of computing. On the other hand, with economic, scientific and military developments, it seems that the increase of intricacy and the size of newly arising problems have no end. We all realize then that the second way, to design good algorithms, will definitely compensate for the hardware limitations in the case of complicated problems. It is the collective and parallel computation property of artificial neural net works that has activated the enthusiasm of researchers in the field of computer science and applied mathematics. It is hard to say that artificial neural networks are solvers of the above-mentioned dilemma, but at least they throw some new light on the difficulties we face. We not only anticipate that there will be neural computers with intelligence but we also believe that the research results of artificial neural networks might lead to new algorithms on von Neumann's computers.
R 4,667.96
State of the Art in Global Optimization
Optimization problems abound in most fields of science, engineering, and tech nology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solving large classes of problems from diverse areas such as engineering design and control, computational chemistry and biology, structural optimization, computer science, operations research, and economics. This book contains refereed invited papers presented at the conference on "State of the Art in Global Optimization: Computational Methods and Applications" held at Princeton University, April 28-30, 1995. The conference presented current re search on global optimization and related applications in science and engineering. The papers included in this book cover a wide spectrum of approaches for solving global optimization problems and applications.
R 6,060.04
