Amir Hossein Gandomi – författare

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.

Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.- Focuses on the introduction and analysis of key algorithms- Includes case studies for real-world applications- Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains.

The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.

Presented by world-renowned researchers and practitioners in metaheuristics Includes techniques, algorithms, and applications based on real-world case studies Presents the methodology for formulating optimization problems for metaheuristics Provides readers with methods for analyzing and tuning the performance of a metaheuristic, as well as for integrating metaheuristics in other AI techniques Features online complementary source code from the applications and algorithms

Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains.The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.- Presented by world-renowned researchers and practitioners in metaheuristics- Includes techniques, algorithms, and applications based on real-world case studies- Presents the methodology for formulating optimization problems for metaheuristics- Provides readers with methods for analyzing and tuning the performance of a metaheuristic, as well as for integrating metaheuristics in other AI techniques- Features online complementary source code from the applications and algorithms

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.Provides comprehensive coverage of advanced intelligence methodsIncludes real-world examples and case studies that illustrate the application of these methods across a wide range of fieldsBegins with an introduction to Deep Learning concepts and quickly moves to the most leading-edge topics in computational intelligence, all with an application to data science techniques

Smart Cities: A System-of-Systems Perspective explores System-of-Systems Engineering (SoSE) in smart cities. Sections address how analytics and innovative technologies can transform urban environments, equipping readers with the knowledge to optimize city operations, enhance public services, and promote sustainable urban growth. The book begins with the foundational concepts of SoSE and key principles of smart cities, exploring emergent behavior and interoperability. It then examines the integration of SoSE within urban infrastructure, highlighting the roles of IoT, AI, and machine learning. Subsequent sections focus on core technologies and analytical tools, including data analytics, predictive modeling, and decision support systems.Readers will also discover innovative solutions to pressing urban challenges, such as sustainability, cybersecurity, and governance, alongside detailed case studies showcasing successful smart city initiatives from around the globe. This book is invaluable for academics and researchers in urban studies, engineering, analytics, IoT, and AI, as well as data scientists and analytics professionals working with urban datasets. Graduate and postgraduate students in engineering, analytics, computer science, or urban studies will also find it a crucial resource.Covers both theoretical foundations and practical methodologiesUses real-world examples to make the concepts accessible and actionableProvides innovative strategies and tools to address the challenges faced by urban planners and system engineers, such as interoperability between systems, scalability, sustainability, and emergent behavior managementIncludes detailed, practical case studies of successful smart city projects worldwide, showcasing best practices, challenges, and lessons learned and offering valuable insights for replicating and adapting smart city solutionsExplores cutting-edge technologies such as AI, IoT, 5G, and renewable energy integration, providing readers with an understanding of how these innovations are transforming urban systems and highlighting future trends and opportunities in smart city developmentOffers decision-making and policy support frameworks, optimization techniques, and governance models to help readers navigate the complexities of multi-stakeholder collaboration