Computational Intelligence In Expensive Optimization Problems

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Computational Intelligence in Expensive Optimization Problems

Author : Yoel Tenne
Publisher : Springer Science & Business Media
ISBN 13 : 364210701X
Total Pages : 800 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Computational Intelligence in Expensive Optimization Problems by : Yoel Tenne

Download or read book Computational Intelligence in Expensive Optimization Problems written by Yoel Tenne and published by Springer Science & Business Media. This book was released on 2010-03-10 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computational Intelligence in Optimization

Author : Yoel Tenne
Publisher : Springer Science & Business Media
ISBN 13 : 3642127754
Total Pages : 412 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Computational Intelligence in Optimization by : Yoel Tenne

Download or read book Computational Intelligence in Optimization written by Yoel Tenne and published by Springer Science & Business Media. This book was released on 2010-06-30 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

High-Performance Simulation-Based Optimization

Author : Thomas Bartz-Beielstein
Publisher : Springer
ISBN 13 : 3030187640
Total Pages : 291 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis High-Performance Simulation-Based Optimization by : Thomas Bartz-Beielstein

Download or read book High-Performance Simulation-Based Optimization written by Thomas Bartz-Beielstein and published by Springer. This book was released on 2019-06-01 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.

Computational Intelligence for Optimization

Author : Nirwan Ansari
Publisher : Springer Science & Business Media
ISBN 13 : 1461563313
Total Pages : 228 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Computational Intelligence for Optimization by : Nirwan Ansari

Download or read book Computational Intelligence for Optimization written by Nirwan Ansari and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of optimization is interdisciplinary in nature, and has been making a significant impact on many disciplines. As a result, it is an indispensable tool for many practitioners in various fields. Conventional optimization techniques have been well established and widely published in many excellent textbooks. However, there are new techniques, such as neural networks, simulated anneal ing, stochastic machines, mean field theory, and genetic algorithms, which have been proven to be effective in solving global optimization problems. This book is intended to provide a technical description on the state-of-the-art development in advanced optimization techniques, specifically heuristic search, neural networks, simulated annealing, stochastic machines, mean field theory, and genetic algorithms, with emphasis on mathematical theory, implementa tion, and practical applications. The text is suitable for a first-year graduate course in electrical and computer engineering, computer science, and opera tional research programs. It may also be used as a reference for practicing engineers, scientists, operational researchers, and other specialists. This book is an outgrowth of a couple of special topic courses that we have been teaching for the past five years. In addition, it includes many results from our inter disciplinary research on the topic. The aforementioned advanced optimization techniques have received increasing attention over the last decade, but relatively few books have been produced.

Foundations of Computational Intelligence Volume 3

Author : Ajith Abraham
Publisher : Springer Science & Business Media
ISBN 13 : 3642010849
Total Pages : 531 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Foundations of Computational Intelligence Volume 3 by : Ajith Abraham

Download or read book Foundations of Computational Intelligence Volume 3 written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2009-04-27 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global optimization is a branch of applied mathematics and numerical analysis that deals with the task of finding the absolutely best set of admissible conditions to satisfy certain criteria / objective function(s), formulated in mathematical terms. Global optimization includes nonlinear, stochastic and combinatorial programming, multiobjective programming, control, games, geometry, approximation, algorithms for parallel architectures and so on. Due to its wide usage and applications, it has gained the attention of researchers and practitioners from a plethora of scientific domains. Typical practical examples of global optimization applications include: Traveling salesman problem and electrical circuit design (minimize the path length); safety engineering (building and mechanical structures); mathematical problems (Kepler conjecture); Protein structure prediction (minimize the energy function) etc. Global Optimization algorithms may be categorized into several types: Deterministic (example: branch and bound methods), Stochastic optimization (example: simulated annealing). Heuristics and meta-heuristics (example: evolutionary algorithms) etc. Recently there has been a growing interest in combining global and local search strategies to solve more complicated optimization problems. This edited volume comprises 17 chapters, including several overview Chapters, which provides an up-to-date and state-of-the art research covering the theory and algorithms of global optimization. Besides research articles and expository papers on theory and algorithms of global optimization, papers on numerical experiments and on real world applications were also encouraged. The book is divided into 2 main parts.

Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering

Author : Gustavo Mendes Platt
Publisher : Springer
ISBN 13 : 331996433X
Total Pages : 284 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering by : Gustavo Mendes Platt

Download or read book Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering written by Gustavo Mendes Platt and published by Springer. This book was released on 2018-09-25 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on metaheuristic methods and its applications to real-world problems in Engineering. The first part describes some key metaheuristic methods, such as Bat Algorithms, Particle Swarm Optimization, Differential Evolution, and Particle Collision Algorithms. Improved versions of these methods and strategies for parameter tuning are also presented, both of which are essential for the practical use of these important computational tools. The second part then applies metaheuristics to problems, mainly in Civil, Mechanical, Chemical, Electrical, and Nuclear Engineering. Other methods, such as the Flower Pollination Algorithm, Symbiotic Organisms Search, Cross-Entropy Algorithm, Artificial Bee Colonies, Population-Based Incremental Learning, Cuckoo Search, and Genetic Algorithms, are also presented. The book is rounded out by recently developed strategies, or hybrid improved versions of existing methods, such as the Lightning Optimization Algorithm, Differential Evolution with Particle Collisions, and Ant Colony Optimization with Dispersion – state-of-the-art approaches for the application of computational intelligence to engineering problems. The wide variety of methods and applications, as well as the original results to problems of practical engineering interest, represent the primary differentiation and distinctive quality of this book. Furthermore, it gathers contributions by authors from four countries – some of which are the original proponents of the methods presented – and 18 research centers around the globe.

Intelligent Computational Optimization in Engineering

Author : Mario Köppen
Publisher : Springer
ISBN 13 : 3642217052
Total Pages : 400 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Intelligent Computational Optimization in Engineering by : Mario Köppen

Download or read book Intelligent Computational Optimization in Engineering written by Mario Köppen and published by Springer. This book was released on 2011-07-15 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches. This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.

Particle Swarm Optimization and Intelligence: Advances and Applications

Author : Parsopoulos, Konstantinos E.
Publisher : IGI Global
ISBN 13 : 1615206671
Total Pages : 328 pages
Book Rating : 4.6/5 (152 download)

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Book Synopsis Particle Swarm Optimization and Intelligence: Advances and Applications by : Parsopoulos, Konstantinos E.

Download or read book Particle Swarm Optimization and Intelligence: Advances and Applications written by Parsopoulos, Konstantinos E. and published by IGI Global. This book was released on 2010-01-31 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents the most recent and established developments of Particle swarm optimization (PSO) within a unified framework by noted researchers in the field"--Provided by publisher.

Handbook of AI-based Metaheuristics

Author : Anand J. Kulkarni
Publisher : CRC Press
ISBN 13 : 1000434257
Total Pages : 584 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Handbook of AI-based Metaheuristics by : Anand J. Kulkarni

Download or read book Handbook of AI-based Metaheuristics written by Anand J. Kulkarni and published by CRC Press. This book was released on 2021-09-01 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms. The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms. This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Multi-Objective Optimization using Artificial Intelligence Techniques

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3030248356
Total Pages : 58 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Multi-Objective Optimization using Artificial Intelligence Techniques by : Seyedali Mirjalili

Download or read book Multi-Objective Optimization using Artificial Intelligence Techniques written by Seyedali Mirjalili and published by Springer. This book was released on 2019-07-24 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computational Intelligence

Author : Jonathan Garibaldi
Publisher : Springer Nature
ISBN 13 : 3031462211
Total Pages : 264 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Computational Intelligence by : Jonathan Garibaldi

Download or read book Computational Intelligence written by Jonathan Garibaldi and published by Springer Nature. This book was released on 2023-11-02 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes a set of selected revised and extended versions of the best papers presented at the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) – held as an online event, from October 25 to 27, 2021. We focus on three outstanding fields of Computational Intelligence through the selected panel, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. Besides presenting the recent advances of the selected areas, the book aims to aggregate new and innovative solutions for confirmed researchers and on the other hand to provide a source of information and/or inspiration for young interested researchers or learners in the ever-expanding and current field of Computational Intelligence. It constitutes a precious provision of knowledge for individual researchers as well as represent a valuable sustenance for collective use in academic libraries (of universities and engineering schools) relating innovative techniques in various fields of applications.

Computational Intelligence and Intelligent Systems

Author : Kangshun Li
Publisher : Springer
ISBN 13 : 9811003564
Total Pages : 737 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Computational Intelligence and Intelligent Systems by : Kangshun Li

Download or read book Computational Intelligence and Intelligent Systems written by Kangshun Li and published by Springer. This book was released on 2016-01-18 with total page 737 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Symposium on Intelligence Computation and Applications, ISICA 2015, held in Guangzhou, China, in November 2015. The 77 revised full papers presented were carefully reviewed and selected from 189 submissions. The papers feature the most up-to-date research in analysis and theory of evolutionary computation, neural network architectures and learning; neuro-dynamics and neuro-engineering; fuzzy logic and control; collective intelligence and hybrid systems; deep learning; knowledge discovery; learning and reasoning.

Applied Computational Intelligence and Soft Computing in Engineering

Author : Khalid, Saifullah
Publisher : IGI Global
ISBN 13 : 1522531300
Total Pages : 340 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Applied Computational Intelligence and Soft Computing in Engineering by : Khalid, Saifullah

Download or read book Applied Computational Intelligence and Soft Computing in Engineering written by Khalid, Saifullah and published by IGI Global. This book was released on 2017-09-13 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although computational intelligence and soft computing are both well-known fields, using computational intelligence and soft computing in conjunction is an emerging concept. This combination can effectively be used in practical areas of various fields of research. Applied Computational Intelligence and Soft Computing in Engineering is an essential reference work featuring the latest scholarly research on the concepts, paradigms, and algorithms of computational intelligence and its constituent methodologies such as evolutionary computation, neural networks, and fuzzy logic. Including coverage on a broad range of topics and perspectives such as cloud computing, sampling in optimization, and swarm intelligence, this publication is ideally designed for engineers, academicians, technology developers, researchers, and students seeking current research on the benefits of applying computation intelligence techniques to engineering and technology.

A Brief Introduction to Continuous Evolutionary Optimization

Author : Oliver Kramer
Publisher : Springer Science & Business Media
ISBN 13 : 3319034227
Total Pages : 94 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis A Brief Introduction to Continuous Evolutionary Optimization by : Oliver Kramer

Download or read book A Brief Introduction to Continuous Evolutionary Optimization written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2013-12-04 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.

Metaheuristics for Dynamic Optimization

Author : Enrique Alba
Publisher : Springer
ISBN 13 : 3642306659
Total Pages : 400 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Metaheuristics for Dynamic Optimization by : Enrique Alba

Download or read book Metaheuristics for Dynamic Optimization written by Enrique Alba and published by Springer. This book was released on 2012-08-11 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay.

Learning and Intelligent Optimization

Author : Carlos A. Coello-Coello
Publisher : Springer
ISBN 13 : 3642255663
Total Pages : 636 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Learning and Intelligent Optimization by : Carlos A. Coello-Coello

Download or read book Learning and Intelligent Optimization written by Carlos A. Coello-Coello and published by Springer. This book was released on 2011-11-15 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011. The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.

Enhancing Surrogate-Based Optimization Through Parallelization

Author : Frederik Rehbach
Publisher : Springer Nature
ISBN 13 : 3031306090
Total Pages : 123 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Enhancing Surrogate-Based Optimization Through Parallelization by : Frederik Rehbach

Download or read book Enhancing Surrogate-Based Optimization Through Parallelization written by Frederik Rehbach and published by Springer Nature. This book was released on 2023-05-29 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.