Computational Optimization and Applications in Engineering and Industry [electronic resource] / edited by Xin-She Yang, Slawomir Koziel.

Adjoint-Based Control of Model and Discretization Errors for Gas and Water Supply Networks -- Derivative-Free Optimization for Oil Field Operations -- Simulation-Driven Design in Microwave Engineering: Application Case Studies -- Airfoil Shape Optimization Using Variable-Fidelity Modeling and Shape-Preserving Response Prediction -- Evolutionary Optimisation Techniques to Estimate Input Parameters in Environmental Emergency Modelling -- Harmony Search Algorithms in Structural Engineering -- Waveform Optimization for Integrated Radar and Communication Systems Using Meta-Heuristic Algorithms -- Parameter Estimation from Laser Flash Experiment Data -- Applications of Computational Intelligence in Behavior Simulation -- A New Approach to Network Optimization Using Chaos-Genetic Algorithm.

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.