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Computational Optimization, Methods and Algorithms (Paperback, Softcover reprint of the original 1st ed. 2011)
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Computational Optimization, Methods and Algorithms (Paperback, Softcover reprint of the original 1st ed. 2011)
Series: Studies in Computational Intelligence, 356
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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.
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