Constraint Handling in Metaheuristics and Applications, Kulkarni Anand J., Mezura-Montes Efrйn, Wang Yong
Автор: de Sourav, Dey Sandip, Bhattacharyya Siddhartha Название: Recent Advances in Hybrid Metaheuristics for Data Clustering ISBN: 1119551595 ISBN-13(EAN): 9781119551591 Издательство: Wiley Рейтинг: Цена: 16624.00 р. Наличие на складе: Поставка под заказ.
Описание:
An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques
Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors--noted experts on the topic--provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering.
The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text:
Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts
Offers an in-depth analysis of a range of optimization algorithms
Highlights a review of data clustering
Contains a detailed overview of different standard metaheuristics in current use
Presents a step-by-step guide to the build-up of hybrid metaheuristics
Offers real-life case studies and applications
Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.
Автор: Kaushik Kumar and J. Paulo Davim Название: Optimization using evolutionary algorithms and metaheuristics ISBN: 0367260441 ISBN-13(EAN): 9780367260446 Издательство: Taylor&Francis Рейтинг: Цена: 25265.00 р. Наличие на складе: Нет в наличии.
Описание: This book covers developments and advances of algorithm based optimization techniques These techniques were only used for non-engineering problems. This book applies them to engineering problems.
Описание: This handbook reflects the tremendous growth in the field, over the past two decades. Through contributions from leading experts, this handbook provides a comprehensive introduction to the underlying theory and methodologies, as well as the various applications of approximation algorithms and metaheuristics.
Описание: This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems.
Автор: Feyel Название: Robust Control Optimization with Metaheuristics ISBN: 1786300427 ISBN-13(EAN): 9781786300423 Издательство: Wiley Рейтинг: Цена: 22010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the automotive industry, a Control Engineer must design a unique control law that is then tested and validated on a single prototype with a level of reliability high enough to to meet a number of complex specifications on various systems.
Описание: This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, SoMMA 2020, held in Chennai, India, in October 2020. The 12 full papers and 7 short papers presented in this volume were thoroughly reviewed and selected from 40 qualified submissions.
Автор: Jayaraman Valadi; Patrick Siarry Название: Applications of Metaheuristics in Process Engineering ISBN: 3319357042 ISBN-13(EAN): 9783319357041 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Metaheuristics exhibit desirable properties like simplicity, easy parallelizability and ready applicability to different types of optimization problems such as real parameter optimization, combinatorial optimization and mixed integer optimization.
Автор: Oliva Diego, Houssein Essam H., Hinojosa Salvador Название: Metaheuristics in Machine Learning: Theory and Applications ISBN: 3030705412 ISBN-13(EAN): 9783030705411 Издательство: Springer Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Erik Cuevas; Daniel Zald?var; Marco P?rez-Cisneros Название: Advances in Metaheuristics Algorithms: Methods and Applications ISBN: 3030077365 ISBN-13(EAN): 9783030077365 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
Описание: This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions.
Автор: Bhattacharyya Siddhartha Название: Hybrid Metaheuristics: Research and Applications ISBN: 9813270225 ISBN-13(EAN): 9789813270220 Издательство: World Scientific Publishing Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.
This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.
The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.
Описание: During the past decades scheduling has been among the most studied op- mization problemsanditisstillanactiveareaofresearch Schedulingappears in many areas of science, engineering and industry and takes di?erent forms depending on the restrictions and optimization criteria of the operating en- ronments 8]. For instance, in optimization and computer science, scheduling has been de?ned as "the allocation of tasks to resources over time in order to achieve optimality in one or more objective criteria in an e?cient way" and in production as "production schedule, i. e., the planning of the production or the sequence of operations according to which jobs pass through machines and is optimal with respect to certain optimization criteria. " Although there is a standardized form of stating any scheduling problem, namely "e?cient allocation ofn jobs onm machines -which can process no more than one activity at a time- with the objective to optimize some - jective function of the job completion times", scheduling is in fact a family of problems. Indeed, several parameters intervene in the problem de?nition: (a) job characteristics (preemptive or not, precedence constraints, release dates, etc. ); (b) resource environment (single vs. parallel machines, un- lated machines, identical or uniform machines, etc. ); (c) optimization criteria (minimize total tardiness, the number of late jobs, makespan, ?owtime, etc.; maximize resource utilization, etc. ); and, (d) scheduling environment (static vs. dynamic, intheformerthenumberofjobstobeconsideredandtheirready times are available while in the later the number of jobs and their charact- istics change over time).
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