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001 978-3-642-20859-1
003 DE-He213
005 20140220083802.0
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008 110713s2011 gw | s |||| 0|eng d
020 _a9783642208591
_9978-3-642-20859-1
024 7 _a10.1007/978-3-642-20859-1
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aKoziel, Slawomir.
_eeditor.
245 1 0 _aComputational Optimization, Methods and Algorithms
_h[electronic resource] /
_cedited by Slawomir Koziel, Xin-She Yang.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXVI, 284p. 87 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v356
505 0 _aComputational 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.
520 _aComputational 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aYang, Xin-She.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642208584
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v356
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-20859-1
912 _aZDB-2-ENG
999 _c107859
_d107859