Note publique d'information : 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
paprika.idref.fr
data.idref.fr
Documentation
