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2011 | Buch

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Computational Optimization and Applications in Engineering and Industry

herausgegeben von: Xin-She Yang, Slawomir Koziel

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Computational Intelligence

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Adjoint-Based Control of Model and Discretization Errors for Gas and Water Supply Networks

Abstract

We are interested in the simulation and optimization of gas and water transport in networks. Those networks consist of pipes and various other components like compressor/pumping stations and valves. The flow through the pipes can be described by different models based on the Euler equations, including hyperbolic systems of partial differential equations. For the other components, algebraic or ordinary differential equations are used. Depending on the data, different models can be used in different regions of the network. We present a strategy that adaptively applies the models and discretizations, using adjoint-based error estimators to maintain the accuracy of the solution. Finally, we give numerical examples for both types of networks.

Pia Domschke, Oliver Kolb, Jens Lang

Derivative-Free Optimization for Oil Field Operations

Abstract

A variety of optimization problems associated with oil production involve cost functions and constraints that require calls to a subsurface flow simulator. In many situations gradient information cannot be obtained efficiently, or a global search is required. This motivates the use of derivative-free (non-invasive, blackbox) optimization methods. This chapter describes the use of several derivative-free techniques, including generalized pattern search, Hooke-Jeeves direct search, a genetic algorithm, and particle swarm optimization, for three key problems that arise in oil field management. These problems are the optimization of settings (pressure or flow rate) in existing wells, optimization of the locations of new wells, and data assimilation or history matching. The performance of the derivative-free algorithms is shown to be quite acceptable, especially when they are implemented within a distributed computing environment.

David Echeverría Ciaurri, Tapan Mukerji, Louis J. Durlofsky

Simulation-Driven Design in Microwave Engineering: Application Case Studies

Abstract

Application of surrogate-based optimization methods to simulation-driven microwave engineering design is demonstrated. It is essential for the considered techniques that the optimization of the original high-fidelity EM-simulated model is replaced by the iterative optimization of its computationally cheap surrogate. The surrogate is updated using available high-fidelity model data to maintain its prediction capability throughout the optimization process. The surrogate model is constructed from the low-fidelity model which—depending on a particular application case—can be either an equivalent circuit or a coarsely discretized full-wave electromagnetic model. Designs satisfying performance requirements are typically obtained at the cost of just a few evaluations of the high-fidelity model. Here, several surrogate-based design optimization techniques for the use in microwave engineering are discussed. Applications of space mapping, simulation-based tuning, variable-fidelity optimization, as well as various response correction techniques are illustrated. Design examples include planar filters, antennas, and transmission line transitions structures.

Slawomir Koziel, Stanislav Ogurtsov

Airfoil Shape Optimization Using Variable-Fidelity Modeling and Shape-Preserving Response Prediction

Abstract

Shape optimization of airfoils is of primary importance in the design of aircraft and turbomachinery with computational fluid dynamic (CFD) being the major design tool. However, as CFD simulation of the fluid flow past airfoils is computationally expensive, and numerical optimization often requires a large number of simulations with several design variables, direct optimization may not be practical. This chapter describes a computationally efficient and robust methodology for airfoil design. The presented approach replaces the direct optimization of an accurate but computationally expensive high-fidelity airfoil model by an iterative re-optimization of a corrected low-fidelity model. The shape-preserving response prediction technique is utilized to correct the low-fidelity model by aligning the pressure and skin friction distributions of the low-fidelity model with the corresponding distributions of the high-fidelity model. The algorithm requires one evaluation of the high-fidelity CFD model per design iteration. The algorithm is applied to several example case studies at both transonic and high-lift flow conditions.

Slawomir Koziel, Leifur Leifsson

Evolutionary Optimisation Techniques to Estimate Input Parameters in Environmental Emergency Modelling

Abstract

Parameter estimation in environmentalmodelling is essential for input parameters, which are difficult or impossible to measure. Especially in simulations for disaster propagation prediction, where hard real-time constraints have to be met to avoid tragedy, the additionally introduced computational burden of advanced global optimisation algorithms still hampers their use in many cases and poses an ongoing challenge. In this chapter we demonstrate how modifications of a Genetic Algorithm (GA) are able to decrease time-consuming fitness evaluations and hence to speed up parameter calibration. Knowledge from past observed catastrophe behaviour is used to guide the GA during various phases towards promising solution areas resulting in a fast convergence. Together with parallel computing techniques it becomes a viable estimation approach in environmental emergency modelling. Encouraging results were obtained in predicting forest fire spread.

Kerstin Wendt, Mónica Denham, Ana Cortés, Tomàs Margalef

Harmony Search Algorithms in Structural Engineering

Abstract

Harmony search method is widely applied in structural design optimization since its emergence. These applications have shown that harmony search algorithm is robust, effective and reliable optimization method. Within recent years several enhancements are suggested to improve the performance of the algorithm. Among these Mahdavi has presented two versions of harmony search methods. He named these as improved harmony search method and global best harmony search method. Saka and Hasancebi (2009) have suggested adaptive harmony search where the harmony search parameters are adjusted automatically during design iterations. Coelho has proposed improved harmony search method. He suggested an expression for one of the parameters of standard harmony search method. In this chapter, the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC (Load and Resistance Factor Design-American Institute of Steel Corporation). The weight of the steel frame is taken as the objective function to be minimized. Seven different structural optimization algorithms are developed each of which are based on one of the above mentioned versions of harmony search method. Three real size steel frames are designed using each of these algorithms. The optimum designs obtained by these techniques are compared and performance of each version is evaluated.

M. P. Saka, I. Aydogdu, O. Hasancebi, Z. W. Geem

Waveform Optimization for Integrated Radar and Communication Systems Using Meta-Heuristic Algorithms

Abstract

Integration of multiple functions such as navigation and radar tasks with communication applications has attracted substantial interest in recent years. In this chapter, we therefore focus on the waveform optimization for such integrated systems based on Oppermann sequences. These sequences are defined by a number of parameters that can be chosen to design sequence sets for a wide range of performance characteristics. It will be shown that meta-heuristic algorithms are wellsuited to find the optimal parameters for these sequences. The motivation behind the use of biologically inspired heuristic and/or meta-heuristic algorithms is due to their ability to solve large, complex, and dynamic problems

Momin Jamil, Hans-Jürgen Zepernick

Parameter Estimation from Laser Flash Experiment Data

Abstract

Optimisation techniques are commonly used for parameter estimation in a wide variety of applications. The application described here is a laser flash thermal diffusivity experiment on a layered sample where the thermal properties of some of the layers are unknown. The aim is to estimate the unknown properties by minimising, in a least squares sense, the difference between model predictions and measured data. Two optimisation techniques have been applied to the problem. Results suggest that the classical nonlinear least-squares optimiser is more efficient than particle swarm optimisation (PSO) for this type of problem. Results have also highlighted the importance of defining a suitable objective function and choosing appropriate model parameters.

Louise Wright, Xin-She Yang, Clare Matthews, Lindsay Chapman, Simon Roberts

Applications of Computational Intelligence in Behavior Simulation of Concrete Materials

Abstract

The application of Computational Intelligence (CI) to structural engineering design problems is relatively new. This chapter presents the use of the CI techniques, and specifically Genetic Programming (GP) and Artificial Neural Network (ANN) techniques, in behavior modeling of concrete materials. We first introduce two main branches of GP, namely Tree-based Genetic Programming (TGP) and Linear Genetic Programming (LGP), and two variants of ANNs, called Multi Layer Perceptron (MLP) and Radial Basis Function (RBF). The simulation capabilities of these techniques are further demonstrated by applying them to two conventional concrete material cases. The first case is simulation of concrete compressive strength using mix properties and the second problem is prediction of elastic modulus of concrete using its compressive strength.

Amir Hossein Gandomi, Amir Hossein Alavi

A New Approach to Network Optimization Using Chaos-Genetic Algorithm

Abstract

Genetic Algorithms (GAs) have been widely used to solve network optimization problems with varying degrees of success. Part of the problem with GAs lies in the premature convergence when dealing with large-scale and complex problems; Caught in local optima, the algorithm might fail to reach the global optimum even after a large number of iterations. In order to overcome the problems with traditional GAs, a method is proposed to integrate Chaos Optimization Algorithms (COAs) with GA to fully exploit their respective searching advantages. The basic idea of COA is to transform the problem variables, by way of a map, from the solution space to a chaos space and to perform a search that benefits from the randomness, orderliness and ergodicity of chaos variable. In this chapter, we will first discuss network optimization in general, and then focus on how chaos theory can be incorporated into the GA in order to enhance its optimization capacities. We will also examine the efficiency of the proposed Chaos-Genetic algorithm in the context of two different types of network optimization problems, Grid scheduling and Network-on-Chip mapping problem.

Golnar Gharooni-fard, Fahime Moein-darbari

Backmatter

Titel: Computational Optimization and Applications in Engineering and Industry
herausgegeben von: Xin-She Yang
Slawomir Koziel
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-20986-4
Print ISBN: 978-3-642-20985-7
DOI: https://doi.org/10.1007/978-3-642-20986-4

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Über dieses Buch

Inhaltsverzeichnis

Frontmatter

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 of Concrete Materials

A New Approach to Network Optimization Using Chaos-Genetic Algorithm

Backmatter

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