Advances in Metaheuristics Algorithms: Methods and Applications
Advances in Metaheuristics Algorithms: Methods and Applications

Author: Erik Cuevas

Publisher: Springer

Published: 2018-04-10

Total Pages: 229

ISBN-13: 3319893092

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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.

Meta-Heuristics
Meta-Heuristics

Author: Stefan Voß

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 513

ISBN-13: 1461557755

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Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimizations comprises a carefully refereed selection of extended versions of the best papers presented at the Second Meta-Heuristics Conference (MIC 97). The selected articles describe the most recent developments in theory and applications of meta-heuristics, heuristics for specific problems, and comparative case studies. The book is divided into six parts, grouped mainly by the techniques considered. The extensive first part with twelve papers covers tabu search and its application to a great variety of well-known combinatorial optimization problems (including the resource-constrained project scheduling problem and vehicle routing problems). In the second part we find one paper where tabu search and simulated annealing are investigated comparatively and two papers which consider hybrid methods combining tabu search with genetic algorithms. The third part has four papers on genetic and evolutionary algorithms. Part four arrives at a new paradigm within meta-heuristics. The fifth part studies the behavior of parallel local search algorithms mainly from a tabu search perspective. The final part examines a great variety of additional meta-heuristics topics, including neural networks and variable neighbourhood search as well as guided local search. Furthermore, the integration of meta-heuristics with the branch-and-bound paradigm is investigated.

Advancements in Applied Metaheuristic Computing
Advancements in Applied Metaheuristic Computing

Author: Dey, Nilanjan

Publisher: IGI Global

Published: 2017-11-30

Total Pages: 357

ISBN-13: 1522541527

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Metaheuristic algorithms are present in various applications for different domains. Recently, researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems. Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing, genetic programming, and medical imaging, this publication is ideal for researchers, academicians, advanced-level students, and technology developers seeking current research on the use of optimization algorithms in several applications.

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance
Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Author: Vasant, Pandian M.

Publisher: IGI Global

Published: 2012-09-30

Total Pages: 735

ISBN-13: 1466620870

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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.

Hybrid Genetic Optimization for IC Chips Thermal Control
Hybrid Genetic Optimization for IC Chips Thermal Control

Author: Mathew V. K.

Publisher: CRC Press

Published: 2022-06-07

Total Pages: 162

ISBN-13: 1000590437

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The continuous miniaturization of integrated circuit (IC) chips and the increase in the sleekness of the design of electronic components have led to the monumental rise of volumetric heat generation in electronic components. Hybrid Genetic Optimization for IC Chips Thermal Control: With MATLAB® Applications focuses on the detailed optimization strategy carried out to enhance the performance (temperature control) of the IC chips oriented at different positions on a switch-mode power supply (SMPS) board and cooled using air under various heat transfer modes. Seven asymmetric protruding IC chips mounted at different positions on an SMPS board are considered in the present study that is supplied with non-uniform heat fluxes. Key Features: Provides guidance on performance enhancement and reliability of IC chips Provides a detailed hybrid optimization strategy for the optimal arrangement of IC chips on a board The MATLAB program for the hybrid optimization strategy along with its stability analysis is carried out in a detailed manner Enables thermal design engineers to identify the positioning of IC chips on the board to increase their reliability and working cycle

Metaheuristic Algorithms in Industry 4.0
Metaheuristic Algorithms in Industry 4.0

Author: Pritesh Shah

Publisher: CRC Press

Published: 2021-09-29

Total Pages: 302

ISBN-13: 1000435989

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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.

Nature-Inspired Methods for Metaheuristics Optimization
Nature-Inspired Methods for Metaheuristics Optimization

Author: Fouad Bennis

Publisher: Springer Nature

Published: 2020-01-17

Total Pages: 503

ISBN-13: 3030264580

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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.

Metaheuristics
Metaheuristics

Author: Karl F. Doerner

Publisher: Springer Science & Business Media

Published: 2007-08-13

Total Pages: 409

ISBN-13: 0387719210

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This book’s aim is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.

Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches
Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches

Author: Yin, Peng-Yeng

Publisher: IGI Global

Published: 2012-10-31

Total Pages: 375

ISBN-13: 146662146X

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Developments in metaheuristics continue to advance computation beyond its traditional methods. With groundwork built on multidisciplinary research findings; metaheuristics, algorithms, and optimization approaches uses memory manipulations in order to take full advantage of strategic level problem solving. Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches provides insight on the latest advances and analysis of technologies in metaheuristics computing. Offering widespread coverage on topics such as genetic algorithms, differential evolution, and ant colony optimization, this book aims to be a forum researchers, practitioners, and students who wish to learn and apply metaheuristic computing.

Constraint Handling in Cohort Intelligence Algorithm
Constraint Handling in Cohort Intelligence Algorithm

Author: Ishaan R. Kale

Publisher: CRC Press

Published: 2021-12-26

Total Pages: 207

ISBN-13: 100052048X

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Mechanical Engineering domain problems are generally complex, consisting of different design variables and constraints. These problems may not be solved using gradient-based optimization techniques. The stochastic nature-inspired optimization techniques have been proposed in this book to efficiently handle the complex problems. The nature-inspired algorithms are classified as bio-inspired, swarm, and physics/chemical-based algorithms. Socio-inspired is one of the subdomains of bio-inspired algorithms, and Cohort Intelligence (CI) models the social tendencies of learning candidates with an inherent goal to achieve the best possible position. In this book, CI is investigated by solving ten discrete variable truss structural problems, eleven mixed variable design engineering problems, seventeen linear and nonlinear constrained test problems and two real-world applications from manufacturing domain. Static Penalty Function (SPF) is also adopted to handle the linear and nonlinear constraints, and limitations in CI and SPF approaches are examined. Constraint Handling in Cohort Intelligence Algorithm is a valuable reference to practitioners working in the industry as well as to students and researchers in the area of optimization methods.