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Contemporary design in engineering and industry relies heavily on computer simulation and efficient algorithms to reduce the cost and to maximize the performance and sustainability as well as profits and energy efficiency. Solving an optimization problem correctly and efficiently requires not only the right choice of optimization algorithms and simulation methods, but also the proper implementation and insight into the problem of interest. This book consists of ten self-contained, detailed case studies of real-world optimization problems, selected from a wide range of applications and contributed from worldwide experts who are working in these exciting areas.
Optimization topics and applications include gas and water supply networks, oil field production optimization, microwave engineering, aerodynamic shape design, environmental emergence modelling, structural engineering, waveform design for radar and communication systems, parameter estimation in laser experiment and measurement, engineering materials and network scheduling. These case studies have been solved using a wide range of optimization techniques, including particle swarm optimization, genetic algorithms, artificial bee colony, harmony search, adaptive error control, derivative-free pattern search, surrogate-based optimization, variable-fidelity modelling, as well as various other methods and approaches. This book is a practical guide to help graduates and researchers to carry out optimization for real-world applications. More advanced readers will also find it a helpful reference and aide memoire.
Optimization topics and applications include gas and water supply networks, oil field production optimization, microwave engineering, aerodynamic shape design, environmental emergence modelling, structural engineering, waveform design for radar and communication systems, parameter estimation in laser experiment and measurement, engineering materials and network scheduling. These case studies have been solved using a wide range of optimization techniques, including particle swarm optimization, genetic algorithms, artificial bee colony, harmony search, adaptive error control, derivative-free pattern search, surrogate-based optimization, variable-fidelity modelling, as well as various other methods and approaches. This book is a practical guide to help graduates and researchers to carry out optimization for real-world applications. More advanced readers will also find it a helpful reference and aide memoire.