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Meta-Heuristic Algorithms for Advanced Distributed Systems

Author: Rohit Anand

Publisher: John Wiley & Sons

Published: 2024-03-12

Total Pages: 469

ISBN-13: 1394188080

META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems—generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: Analysis of security issues, distributed system design, stochastic optimization techniques, and more Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.

Meta-Heuristic Algorithms for Advanced Distributed Systems

Author: Rohit Anand

Publisher: John Wiley & Sons

Published: 2024-04-02

Total Pages: 469

ISBN-13: 1394188064

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Metaheuristics for Scheduling in Distributed Computing Environments

Author: Fatos Xhafa

Publisher: Springer

Published: 2008-09-08

Total Pages: 373

ISBN-13: 3540692770

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Grid computing has emerged as one of the most promising computing paradigms of the new millennium! Achieving high performance Grid computing requires techniques to efficiently and adaptively allocate jobs and applications to available resources in a large scale, highly heterogenous and dynamic environment. This volume presents meta-heuristics approaches for Grid scheduling problems. Due to the complex nature of the problem, meta-heuristics are primary techniques for the design and implementation of efficient Grid schedulers. The volume brings new ideas, analysis, implementations and evaluation of meta-heuristic techniques for Grid scheduling, which make this volume novel in several aspects. The 14 chapters of this volume have identified several important formulations of the problem, which we believe will serve as a reference for the researchers in the Grid computing community. Important features include the detailed overview of the various novel metaheuristic scheduling approaches, excellent coverage of timely, advanced scheduling topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and scheduling will find the comprehensive coverage of this book invaluable.

Advanced Distributed Systems

Author: Felix F. Ramos

Publisher: Springer Science & Business Media

Published: 2005-09-15

Total Pages: 569

ISBN-13: 3540280634

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This book constitutes the thoroughly refereed post-proceedings of the Fifth International School and Symposium on Advanced Distributed Systems, ISSADS 2005, held in Guadalajara, Mexico in January 2005. The 50 revised full papers presented were carefully reviewed and selected from over 100 submissions. The papers are organized in topical sections on database systems, distributed and parallel algorithms, real-time distributed systems, cooperative information systems, fault tolerance, information retrieval, modeling and simulation, wireless networks and mobile computing, artificial life and multi agent systems.

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Author: Ali Kaveh

Publisher: Springer Nature

Published: 2021-01-21

Total Pages: 890

ISBN-13: 3030593924

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This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his graduate students, consisting of Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Democratic Particle Swarm Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which are developed by other authors and have been successfully applied to various optimization problems. These consist of Partical Swarm Optimization, Big Band Big Crunch algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm and Chaos Embedded Metaheuristic Algorithm. Finally a multi-objective Optimization is presented to Solve large scale structural problems based on the Charged System Search algorithm, In the second edition seven new chapters are added consisting of Enhance colliding bodies optimization, Global sensitivity analysis, Tug of War Optimization, Water evaporation optimization, Vibrating System Optimization and Cyclical Parthenogenesis Optimization algorithm. In the third edition, five new chapters are included consisting of the recently developed algorithms. These are Shuffled Shepherd Optimization Algorithm, Set Theoretical Shuffled Shepherd Optimization Algorithm, Set Theoretical Teaching-Learning-Based Optimization Algorithm, Thermal Exchange Metaheuristic Optimization Algorithm, and Water Strider Optimization Algorithm and Its Enhancement. The concepts and algorithm presented in this book are not only applicable to optimization of skeletal structure, finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks.

Metaheuristics Algorithms for Medical Applications

Author: Mohamed Abdel-Basset

Publisher: Elsevier

Published: 2023-11-25

Total Pages: 249

ISBN-13: 0443133158

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Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing Metaheuristics techniques with Machine Learning for solving biomedical problems. The book is organized to present a stepwise progression beginning with the basics of Metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of the book presents the fundamental concepts of Metaheuristics and Machine Learning, and also provides a comprehensive taxonomic view of Metaheuristics methods according to a variety of criteria such as data type, scope, method, and so forth. The second section of the book explains how to apply Metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in Metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in Metaheuristics for biomedical science. The book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice. Introduces a new set of Metaheuristics techniques for biomedical applications Presents basic concepts of Metaheuristics, methods and practices, followed by advanced topics and applications Provides researchers, practitioners, and project stakeholders with a complete guide for understanding and applying metaheuristics and machine learning techniques in their projects and solutions

Machine Learning and Optimization Models for Optimization in Cloud

Author: Punit Gupta

Publisher: CRC Press

Published: 2022-02-17

Total Pages: 232

ISBN-13: 1000542254

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Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Metaheuristics

Author: El-Ghazali Talbi

Publisher: John Wiley & Sons

Published: 2009-05-27

Total Pages: 625

ISBN-13: 0470496908

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A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Author: A. Kaveh

Publisher: Springer Science & Business

Published: 2014-04-28

Total Pages: 433

ISBN-13: 3319055496

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This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks.

Metaheuristic Optimization in Power Engineering

Author: Professor Jordan Radosavljevic

Publisher:

Published: 2024-11

Total Pages: 0

ISBN-13: 9781837241378

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A new edition in two volumes of the systematic and comprehensive reference on metaheuristic methods for power systems with distributed renewables, which offers MATLAB-based software, with revised and new chapters.