Data-Driven Evolutionary Optimization

Data-Driven Evolutionary Optimization

Author: Yaochu Jin

Publisher: Springer Nature

Published: 2021-06-28

Total Pages: 393

ISBN-13: 3030746402

DOWNLOAD EBOOK

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.


Multi-Objective Optimization using Evolutionary Algorithms

Multi-Objective Optimization using Evolutionary Algorithms

Author: Kalyanmoy Deb

Publisher: John Wiley & Sons

Published: 2001-07-05

Total Pages: 540

ISBN-13: 9780471873396

DOWNLOAD EBOOK

Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.


Innovization

Innovization

Author: Kalyanmoy Deb

Publisher: Springer

Published: 2016-06-12

Total Pages: 300

ISBN-13: 9783540731726

DOWNLOAD EBOOK

Every designer wants to know what makes a product or process optimal. This book suggests a holistic approach to optimization that involves two steps: find a set of trade-off optimal solutions involving two or more conflicting objectives related to the problem, and then analyze these high-performing solutions to determine solution principles that commonly prevail among these solutions. Since the solutions are optimal, such common principles are likely to exist; and since these principles are common to many solutions they are likely to provide robust, reliable solution principles. The author is one of the leading researchers in multiobjective optimization, and an expert in design methodology. In this book he offers introductions to innovation in design; multiobjective optimization, in particular evolutionary multiobjective optimization (EMO) techniques that find multiple, trade-off, optimal solutions; and knowledge extraction from multivariate data using graphical, regression and clustering techniques. He then introduces his innovization methodology for revealing new, innovative design principles related to decision variables and objectives, and he demonstrates it through engineering case studies, in particular product and process design problems. The book will be of benefit to practitioners, researchers and students engaged with issues of optimal design, in particular in domains such as engineering design, product design, engineering optimization, manufacturing, process design and complex systems. The sample computer code referenced is available from the author's website.


Multidisciplinary Design Optimization

Multidisciplinary Design Optimization

Author: Natalia M. Alexandrov

Publisher: SIAM

Published: 1997-01-01

Total Pages: 476

ISBN-13: 9780898713596

DOWNLOAD EBOOK

Multidisciplinary design optimization (MDO) has recently emerged as a field of research and practice that brings together many previously disjointed disciplines and tools of engineering and mathematics. MDO can be described as a technology, environment, or methodology for the design of complex, coupled engineering systems, such as aircraft, automobiles, and other mechanisms, the behavior of which is determined by interacting subsystems.


Multi-Objective Optimization using Artificial Intelligence Techniques

Multi-Objective Optimization using Artificial Intelligence Techniques

Author: Seyedali Mirjalili

Publisher: Springer

Published: 2019-07-24

Total Pages: 66

ISBN-13: 3030248356

DOWNLOAD EBOOK

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.


Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems

Author: Carlos Coello Coello

Publisher: Springer Science & Business Media

Published: 2007-08-26

Total Pages: 810

ISBN-13: 0387367977

DOWNLOAD EBOOK

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.


Evolutionary Multiobjective Optimization

Evolutionary Multiobjective Optimization

Author: Ajith Abraham

Publisher: Springer Science & Business Media

Published: 2005-09-05

Total Pages: 313

ISBN-13: 1846281377

DOWNLOAD EBOOK

Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.


Multiobjective Optimization

Multiobjective Optimization

Author: Jürgen Branke

Publisher: Springer

Published: 2008-10-18

Total Pages: 481

ISBN-13: 3540889086

DOWNLOAD EBOOK

Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support. This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA). This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.


Computational Intelligence in Expensive Optimization Problems

Computational Intelligence in Expensive Optimization Problems

Author: Yoel Tenne

Publisher: Springer Science & Business Media

Published: 2010-03-10

Total Pages: 736

ISBN-13: 364210701X

DOWNLOAD EBOOK

In modern science and engineering, laboratory experiments are replaced by high fidelity and computationally expensive simulations. Using such simulations reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc. Under such difficulties, classical optimization and analysis methods may perform poorly. This motivates the application of computational intelligence methods such as evolutionary algorithms, neural networks and fuzzy logic, which often perform well in such settings. This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems. Topics covered include: dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic. reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporation of expert systems and human-system interface, single and multiobjective algorithms, data mining and statistical analysis, analysis of real-world cases (such as multidisciplinary design optimization). The edited book provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields. As such, it is a comprehensive reference for researchers, practitioners, and advanced-level students interested in both the theory and practice of using computational intelligence for expensive optimization problems.