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.

After v. 1, each volume's t.p. names a different panel at the beginning of its author statement.

The natural mission of Computational Science is to tackle all sorts of human problems and to work out intelligent automata aimed at alleviating the b- den of working out suitable tools for solving complex problems. For this reason ComputationalScience,thoughoriginatingfromtheneedtosolvethemostch- lenging problems in science and engineering (computational science is the key player in the ?ght to gain fundamental advances in astronomy, biology, che- stry, environmental science, physics and several other scienti?c and engineering disciplines) is increasingly turning its attention to all ?elds of human activity. In all activities, in fact, intensive computation, information handling, kn- ledge synthesis, the use of ad-hoc devices, etc. increasingly need to be exploited and coordinated regardless of the location of both the users and the (various and heterogeneous) computing platforms. As a result the key to understanding the explosive growth of this discipline lies in two adjectives that more and more appropriately refer to Computational Science and its applications: interoperable and ubiquitous. Numerous examples of ubiquitous and interoperable tools and applicationsaregiveninthepresentfourLNCSvolumescontainingthecontri- tions delivered at the 2004 International Conference on Computational Science and its Applications (ICCSA 2004) held in Assisi, Italy, May 14–17, 2004.

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

Spotlighting the field of Multidisciplinary Design Optimization (MDO), this book illustrates and implements state-of-the-art methodologies within the complex process of aerospace system design under uncertainties. The book provides approaches to integrating a multitude of components and constraints with the ultimate goal of reducing design cycles. Insights on a vast assortment of problems are provided, including discipline modeling, sensitivity analysis, uncertainty propagation, reliability analysis, and global multidisciplinary optimization. The extensive range of topics covered include areas of current open research. This Work is destined to become a fundamental reference for aerospace systems engineers, researchers, as well as for practitioners and engineers working in areas of optimization and uncertainty. Part I is largely comprised of fundamentals. Part II presents methodologies for single discipline problems with a review of existing uncertainty propagation, reliability analysis, and optimization techniques. Part III is dedicated to the uncertainty-based MDO and related issues. Part IV deals with three MDO related issues: the multifidelity, the multi-objective optimization and the mixed continuous/discrete optimization and Part V is devoted to test cases for aerospace vehicle design.

The book addresses surrogate-assisted design of antenna arrays, in particular, how surrogate models, both data-driven and physics-based, can be utilized to expedite procedures such as parametric optimization, design closure, statistical analysis, or fault detection. Algorithms and design frameworks are illustrated using a large variety of examples including real-world printed-circuit antenna and antenna array structures.This unique compendium contains introductory materials concerning numerical optimization, both conventional (gradient-based and derivative-free, including metaheuristics) and surrogate-based, as well as a considerable selection of customized procedures developed specifically to handle antenna array problems. Recommendations concerning practical aspects of surrogate-assisted multi-objective antenna optimization are also given. The methods presented allow for cost-efficient handling of antenna array design problems (involving CPU-intensive EM models) in the context of design optimization and statistical analysis, which will benefit both researchers, designers and graduate students.

This book explores systems-based, co-design, introducing a “Decision-Based, Co-Design” (DBCD) approach for the co-design of materials, products, and processes. In recent years there have been significant advances in modeling and simulation of material behavior, from the smallest atomic scale to the macro scale. However, the uncertainties associated with these approaches and models across different scales need to be addressed to enable decision-making resulting in designs that are robust, that is, relatively insensitive to uncertainties. An approach that facilitates co-design is needed across material, product design and manufacturing processes. This book describes a cloud-based platform to support decisions in the design of engineered systems (CB-PDSIDES), which feature an architecture that promotes co-design through the servitization of decision-making, knowledge capture and use templates that allow previous solutions to be reused. Placing the platform in the cloud aids mass collaboration and open innovation. A valuable reference resource reference on all areas related to the design of materials, products and processes, the book appeals to material scientists, design engineers and all those involved in the emerging interdisciplinary field of integrated computational materials engineering (ICME).

The field of knowledge-based systems (KBS) has expanded enormously during the last years, and many important techniques and tools are currently available. Applications of KBS range from medicine to engineering and aerospace.This book provides a selected set of state-of-the-art contributions that present advanced techniques, tools and applications. These contributions have been prepared by a group of eminent researchers and professionals in the field.The theoretical topics covered include: knowledge acquisition, machine learning, genetic algorithms, knowledge management and processing under uncertainty, conflict detection and resolution, structured knowledge architectures, and natural language-based man-machine communication.The Applications include: Real-time decision support, system fault diagnosis, quality assessment, manufacturing production, robotic assembly, and robotic welding.The reader can save considerable time in searching the scattered literature in the field, and can find here a powerful set of how-to-do issues and results.

Im Mittelpunkt dieses Buches steht eines der wichtigsten Optimierungsverfahren der industriellen Ingenieurtechnik: Mit Hilfe genetischer Algorithmen lassen sich Qualität, Design und Zuverlässigkeit von Produkten entscheidend verbessern. Das Verfahren beruht auf der Wahrscheinlichkeitstheorie und lehnt sich an die Prinzipien der biologischen Vererbung an: Die Eigenschaften des Produkts werden, unter Beachtung der äußeren Randbedingungen, schrittweise optimiert. Ein hochaktueller Band international anerkannter Autoren. (03/00)

Practical Synthesis of High-Performance Analog Circuits presents a technique for automating the design of analog circuits. Market competition and the astounding pace of technological innovation exert tremendous pressure on circuit design engineers to turn ideas into products quickly and get them to market. In digital Application Specific Integrated Circuit (ASIC) design, computer aided design (CAD) tools have substantially eased this pressure by automating many of the laborious steps in the design process, thereby allowing the designer to maximise his design expertise. But the world is not solely digital. Cellular telephones, magnetic disk drives, neural networks and speech recognition systems are a few of the recent technological innovations that rely on a core of analog circuitry and exploit the density and performance of mixed analog/digital ASICs. To maximize profit, these mixed-signal ASICs must also make it to market as quickly as possible. However, although the engineer working on the digital portion of the ASIC can rely on sophisticated CAD tools to automate much of the design process, there is little help for the engineer working on the analog portion of the chip. With the exception of simulators to verify the circuit design when it is complete, there are almost no general purpose CAD tools that an analog design engineer can take advantage of to automate the analog design flow and reduce his time to market. Practical Synthesis of High-Performance Analog Circuits presents a new variation-tolerant analog synthesis strategy that is a significant step towards ending the wait for a practical analog synthesis tool. A new synthesis strategy is presented that can fully automate the path from a circuit topology and performance specifications to a sized variation-tolerant circuit schematic. This strategy relies on asymptotic waveform evaluation to predict circuit performance and simulated annealing to solve a novel non-linear infinite programming optimization formulation of the circuit synthesis problem via a sequence of smaller optimization problems. Practical Synthesis of High-Performance Analog Circuits will be of interest to analog circuit designers, CAD/EDA industry professionals, academics and students.