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 736 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.

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 424 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.

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.

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.

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.

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.

Download or read book Sequential Approximate Multiobjective Optimization Using Computational Intelligence written by Hirotaka Nakayama and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many kinds of practical problems such as engineering design, industrial m- agement and ?nancial investment have multiple objectives con?icting with eachother. Thoseproblemscanbeformulatedasmultiobjectiveoptimization. In multiobjective optimization, there does not necessarily a unique solution which minimizes (or maximizes) all objective functions. We usually face to the situation in which if we want to improve some of objectives, we have to give up other objectives. Finally, we pay much attention on how much to improve some of objectives and instead how much to give up others. This is called “trade-o?. ” Note that making trade-o? is a problem of value ju- ment of decision makers. One of main themes of multiobjective optimization is how to incorporate value judgment of decision makers into decision s- port systems. There are two major issues in value judgment (1) multiplicity of value judgment and (2) dynamics of value judgment. The multiplicity of value judgment is treated as trade-o? analysis in multiobjective optimi- tion. On the other hand, dynamics of value judgment is di?cult to treat. However, it is natural that decision makers change their value judgment even in decision making process, because they obtain new information during the process. Therefore, decision support systems are to be robust against the change of value judgment of decision makers. To this aim, interactive p- grammingmethodswhichsearchasolutionwhileelicitingpartialinformation on value judgment of decision makers have been developed. Those methods are required to perform ?exibly for decision makers’ attitude.

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.

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.

Download or read book Multi objective Optimization in Computational Intelligence written by Lam Thu Bui and published by IGI Global Snippet. This book was released on 2008 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

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.

Download or read book Evolutionary Computation for Dynamic Optimization Problems written by Shengxiang Yang and published by Springer. This book was released on 2011-12-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.

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.

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.

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.

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.

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.