Computational optimization, methods and algorithms
著者
書誌事項
Computational optimization, methods and algorithms
（Studies in computational intelligence, v. 356）
Springer, c2011
大学図書館所蔵 全1件
注記
Includes bibliographical references and indexn
内容説明・目次
内容説明
Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry.
This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.
目次
Computational Optimization: An Overview.-
Optimization Algorithms.-
Surrogate-Based Methods.-
Derivative-Free Optimization.-
Maximum Simulated Likelihood Estimation: Techniques and
Applications in Economics.-
Optimizing Complex Multi-Location Inventory Models Using
Particle Swarm Optimization.-
Traditional and Hybrid Derivative-Free Optimization Approaches
for Black Box Functions.-
Simulation-Driven Design in Microwave Engineering: Methods.-
Variable-Fidelity Aerodynamic Shape Optimization.-
Evolutionary Algorithms Applied to Multi-Objective Aerodynamic
Shape Optimization.-
An Enhanced Support Vector Machines Model for Classification
and Rule Generation.-
Benchmark Problems in Structural Optimization.
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