Metaheuristics For Robotics
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| Author | : Hamouche Oulhadj |
| Publisher | : John Wiley & Sons |
| Total Pages | : 184 |
| Release | : 2020-04-28 |
| Genre | : Computers |
| ISBN | : 1786303809 |
This book is dedicated to the application of metaheuristic optimization in trajectory generation and control issues in robotics. In this area, as in other fields of application, the algorithmic tools addressed do not require a comprehensive list of eligible solutions to effectively solve an optimization problem. This book investigates how, by reformulating the problems to be solved, it is possible to obtain results by means of metaheuristics. Through concrete examples and case studies particularly related to robotics this book outlines the essentials of what is needed to reformulate control laws into concrete optimization data. The resolution approaches implemented as well as the results obtained are described in detail, in order to give, as much as possible, an idea of metaheuristics and their performance within the context of their application to robotics.
| Author | : Sean Luke |
| Publisher | : |
| Total Pages | : 242 |
| Release | : 2012-12-20 |
| Genre | : Algorithms |
| ISBN | : 9781300549628 |
Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 71 figures and 135 algorithms great and small. Algorithms include: Gradient Ascent techniques, Hill-Climbing variants, Simulated Annealing, Tabu Search variants, Iterated Local Search, Evolution Strategies, the Genetic Algorithm, the Steady-State Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Genetic Programming variants, One- and Two-Population Competitive Coevolution, N-Population Cooperative Coevolution, Implicit Fitness Sharing, Deterministic Crowding, NSGA-II, SPEA2, GRASP, Ant Colony Optimization variants, Guided Local Search, LEM, PBIL, UMDA, cGA, BOA, SAMUEL, ZCS, XCS, and XCSF.
| Author | : Diego Oliva |
| Publisher | : Springer Nature |
| Total Pages | : 765 |
| Release | : |
| Genre | : Computational intelligence |
| ISBN | : 3030705420 |
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
| Author | : Pritesh Shah |
| Publisher | : CRC Press |
| Total Pages | : 302 |
| Release | : 2021-09-29 |
| Genre | : Computers |
| ISBN | : 1000435989 |
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces. This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more. Key features: Includes industrial case studies. Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics. surveys current trends and challenges in metaheuristics and industry 4.0. Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.
| Author | : Fouad Bennis |
| Publisher | : Springer Nature |
| Total Pages | : 503 |
| Release | : 2020-01-17 |
| Genre | : Business & Economics |
| ISBN | : 3030264580 |
This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.
| Author | : Anuj Kumar |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 219 |
| Release | : 2022-01-19 |
| Genre | : Computers |
| ISBN | : 3110716259 |
This book offers a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature-inspired algorithms. Their wide applicability makes them a hot research topic and an effi cient tool for the solution of complex optimization problems in various fi elds of sciences, engineering, and in numerous industries.
| Author | : Vasant, Pandian M. |
| Publisher | : IGI Global |
| Total Pages | : 735 |
| Release | : 2012-09-30 |
| Genre | : Computers |
| ISBN | : 1466620870 |
Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.
| Author | : Hamouche Oulhadj |
| Publisher | : John Wiley & Sons |
| Total Pages | : 190 |
| Release | : 2020-02-25 |
| Genre | : Computers |
| ISBN | : 111970698X |
This book is dedicated to the application of metaheuristic optimization in trajectory generation and control issues in robotics. In this area, as in other fields of application, the algorithmic tools addressed do not require a comprehensive list of eligible solutions to effectively solve an optimization problem. This book investigates how, by reformulating the problems to be solved, it is possible to obtain results by means of metaheuristics. Through concrete examples and case studies particularly related to robotics this book outlines the essentials of what is needed to reformulate control laws into concrete optimization data. The resolution approaches implemented as well as the results obtained are described in detail, in order to give, as much as possible, an idea of metaheuristics and their performance within the context of their application to robotics.
| Author | : Johann Dréo |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 373 |
| Release | : 2006-01-16 |
| Genre | : Mathematics |
| ISBN | : 3540309667 |
Contains case studies from engineering and operations research Includes commented literature for each chapter
| Author | : Marco Dorigo |
| Publisher | : MIT Press |
| Total Pages | : 238 |
| Release | : 1998 |
| Genre | : Computers |
| ISBN | : 9780262041645 |
foreword by Lashon Booker To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy,"and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.
