Erik Cuevas - Böcker

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB® Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimizationThe material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies. Book Features: Provides a unified view of the most popular metaheuristic methods currently in use Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems Covers design aspects and implementation in MATLAB® Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimizationThe material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

Agent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores computational implementations and accompanying MATLAB software code as a means of inspiring readers to apply agent-based models to solve a diverse range of problems. It comes with a large amount of software code that accompanies the main text, and the modeling systems described in the book are implemented using MATLAB as the programming language. Despite the heavy mathematical components of Agent-Based Models and complex systems, it is possible to utilize these models without in-depth understanding of their mathematical fundamentals.This book enables computer scientists, mathematicians, researchers, and engineers to apply ABM in a wide range of research and engineering applications. It gradually advances from basic to more advanced methods while reinforcing complex systems through practical, hands-on applications of various computational models.

Considers the most crucial methods and models of Agent-Based Models and complex systemsDemonstrates how to use computational implementations in applying Agent-Based Models to solve a diverse range of scientific research and engineering problemsProvides a wide range of hands-on implementation examples, along with MATLAB software code readers can experiment with and modify

Image processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image processing, establishing the essential groundwork necessary for further explorations covered in Volume 2.Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.

Image processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, this second installment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean shift algorithm, and the application of singular value decomposition for image compression. This second volume also incorporates several important machine learning techniques applied to image processing, building on the foundational knowledge introduced in Volume 1.Written with instructors and students of image processing in mind, this book’s intuitive organization also contains appeal for app developers and engineers.

Image processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation and feature extraction, machine learning learning algorithms are used to interpret the processed data through classification, clustering and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, the first instalment explores the fundamental concepts and techniques in image processing, starting from pixel operations and their properties, exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. The second instalment explores the more advanced concepts and techniques in image processing, including morphological filters, color image processing, image matching, feature-based segmentation utilizing the mean-shift algorithm, and the application of singular value decomposition (SVD) for image compression. Written with instructors and students of image processing in mind, this book’s intuitive organisation also contains appeal for app developers and engineers.

Learn to bridge the gap between machine learning and metaheuristic methods to solve problems in optimization approaches Few areas of technology have greater potential to revolutionize the globe than artificial intelligence. Two key areas of artificial intelligence, machine learning and metaheuristic computation, have an enormous range of individual and combined applications in computer science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. Machine Learning and Metaheuristic Computation offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge artificial intelligence tools. The text also provides: Treatment suitable for readers with only basic mathematical trainingDetailed discussion of topics including dimensionality reduction, clustering methods, differential evolution, and moreA rigorous but accessible vision of machine learning algorithms and the most popular approaches of metaheuristic optimizationMachine Learning and Metaheuristic Computation is ideal for students, researchers, and professionals looking to combine these vital methods to solve problems in optimization approaches.

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems.

This book discusses the use of efficient metaheuristic algorithms to solve diverse power system problems, providing an overview of the various aspects of metaheuristic methods to enable readers to gain a comprehensive understanding of the field and of conducting studies on specific metaheuristic algorithms related to power-system applications. By bridging the gap between recent metaheuristic techniques and novel power system methods that benefit from the convenience of metaheuristic methods, it offers power system practitioners who are not metaheuristic computation researchers insights into the techniques, which go beyond simple theoretical tools and have been adapted to solve important problems that commonly arise. On the other hand, members of the metaheuristic computation community learn how power engineering problems can be translated into optimization tasks, and it is also of interest to engineers and application developers. Further, since each chapter can be read independently, the relevant information can be quickly found. Power systems is a multidisciplinary field that addresses the multiple approaches used for design and analysis in areas ranging from signal processing, and electronics to computational intelligence, including the current trend of metaheuristic computation.

This book presents advances in alternative swarm development that have proved to be effective in several complex problems. Swarm intelligence (SI) is a problem-solving methodology that results from the cooperation between a set of agents with similar characteristics. The study of biological entities, such as animals and insects, manifesting social behavior has resulted in several computational models of swarm intelligence. While there are numerous books addressing the most widely known swarm methods, namely ant colony algorithms and particle swarm optimization, those discussing new alternative approaches are rare. The focus on developments based on the simple modification of popular swarm methods overlooks the opportunity to discover new techniques and procedures that can be useful in solving problems formulated by the academic and industrial communities. Presenting various novel swarm methods and their practical applications, the book helps researchers, lecturers, engineersand practitioners solve their own optimization problems.

This book presents new, alternative metaheuristic developments that have proved to be effective in various complex problems to help researchers, lecturers, engineers, and practitioners solve their own optimization problems.

This book presents new, alternative metaheuristic developments that have proved to be effective in various complex problems to help researchers, lecturers, engineers, and practitioners solve their own optimization problems.

Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments.

Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments.

Most of the problems in science, engineering, economics, and other areas can be translated as an optimization or a search problem.

Most of the problems in science, engineering, economics, and other areas can be translated as an optimization or a search problem.

Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.

Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.

This textbook provides readers a comprehensive introduction to numerical methods, using MATLAB®. This book not only covers the most important methods and techniques of scientific computation, but also contains a great amount of code and implementations, facilitating the process of learning and application.

This textbook provides readers a comprehensive introduction to numerical methods, using MATLAB®. This book not only covers the most important methods and techniques of scientific computation, but also contains a great amount of code and implementations, facilitating the process of learning and application.

Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques.

Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques.

Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods.

This book encompasses three distinct yet interconnected objectives. Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods. Secondly, this book endeavors to systematically assess the performance of well-established algorithms across a spectrum of intricate and real-world problems. Finally, this book serves as a vital resource for the analysis and evaluation of metaheuristic algorithms. It provides a foundational framework for assessing their performance, particularly in terms of the balance between exploration and exploitation, as well as their capacity to obtain optimal solutions. Collectively, these objectives contribute to advancing our understanding of metaheuristic methods and their applicability in addressing diverse and demanding optimization tasks. The materials were compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Additionally, engineering practitioners who are not familiar with metaheuristic computation concepts will appreciate that the techniques discussed are beyond simple theoretical tools because they have been adapted to solve significant problems that commonly arise in engineering areas.

This textbook provides readers with the knowledge and practical skills necessary to understand, design, and construct their own functional DC motors using 3D printing technology.

This textbook provides readers with a comprehensive exploration of optimization techniques in industrial engineering, with a specific focus on the Economic Order Quantity (EOQ) problem. It strikes a unique balance by thoroughly discussing the underlying concepts and theories, equipping the reader with the knowledge needed to develop their own programs for solving complex optimization problems in the field. A distinctive feature of this book is its extensive use of MATLAB implementations, which serves as a practical tool to bridge the gap between theory and real-world application. The book is structured with the understanding that learning is accelerated when theoretical concepts are complemented by practical, code-based problem-solving examples. This approach is particularly beneficial for students who may have a weaker background in mathematics, as it demonstrates the practicality and effectiveness of optimization in a more accessible manner. The inclusion of ready-made code examples not only makes the subject matter more engaging for students but also encourages them to experiment, modify, and enhance the code with their own ideas. This method of learning is designed to be less daunting and more stimulating, particularly for those who might feel overwhelmed by the prospect of developing complex programs from scratch. The book's approach is aimed at demystifying the complexities of optimization in industrial engineering, making it more approachable and interesting for students and practitioners alike.Diverging from other texts that primarily focus on classical techniques for addressing optimization problems in industrial engineering, this book sets itself apart by delving into modern metaheuristic methods. Metaheuristic techniques have gained recognition for their efficacy in tackling complex problems that are often laden with diverse and challenging constraints. These methods, which include algorithms such as simulated annealing, and particleswarm optimization, offer a more dynamic and flexible approach to finding solutions compared to traditional methods. They are particularly adept at navigating vast search spaces and identifying optimal or near-optimal solutions in scenarios where conventional approaches might struggle. This inclusion of metaheuristic methods gives the book a unique quality, providing readers with a comprehensive understanding of both the established foundations and the cutting-edge advancements in the field of optimization. The book's exploration of these advanced techniques not only broadens the reader's knowledge base but also equips them with the tools to effectively solve more intricate and nuanced problems encountered in industrial engineering. This dual focus on classical and modern methods positions the book as a valuable and forward-thinking resource in the realm of industrial optimization.

This textbook provides readers with a comprehensive exploration of optimization techniques in industrial engineering, with a specific focus on the Economic Order Quantity (EOQ) problem. It strikes a unique balance by thoroughly discussing the underlying concepts and theories, equipping the reader with the knowledge needed to develop their own programs for solving complex optimization problems in the field. A distinctive feature of this book is its extensive use of MATLAB implementations, which serves as a practical tool to bridge the gap between theory and real-world application. The book is structured with the understanding that learning is accelerated when theoretical concepts are complemented by practical, code-based problem-solving examples. This approach is particularly beneficial for students who may have a weaker background in mathematics, as it demonstrates the practicality and effectiveness of optimization in a more accessible manner. The inclusion of ready-made code examples not only makes the subject matter more engaging for students but also encourages them to experiment, modify, and enhance the code with their own ideas. This method of learning is designed to be less daunting and more stimulating, particularly for those who might feel overwhelmed by the prospect of developing complex programs from scratch. The book's approach is aimed at demystifying the complexities of optimization in industrial engineering, making it more approachable and interesting for students and practitioners alike.Diverging from other texts that primarily focus on classical techniques for addressing optimization problems in industrial engineering, this book sets itself apart by delving into modern metaheuristic methods. Metaheuristic techniques have gained recognition for their efficacy in tackling complex problems that are often laden with diverse and challenging constraints. These methods, which include algorithms such as simulated annealing, and particleswarm optimization, offer a more dynamic and flexible approach to finding solutions compared to traditional methods. They are particularly adept at navigating vast search spaces and identifying optimal or near-optimal solutions in scenarios where conventional approaches might struggle. This inclusion of metaheuristic methods gives the book a unique quality, providing readers with a comprehensive understanding of both the established foundations and the cutting-edge advancements in the field of optimization. The book's exploration of these advanced techniques not only broadens the reader's knowledge base but also equips them with the tools to effectively solve more intricate and nuanced problems encountered in industrial engineering. This dual focus on classical and modern methods positions the book as a valuable and forward-thinking resource in the realm of industrial optimization.

This book is to explore the development of metaheuristic algorithms over the past decade, focusing on key advancements in their components and structural features, which have driven progress in search techniques.

This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.