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Efficient multi-objective optimization with fitness landscape — A special application to the optimal design of alloy-steels

Efficient multi-objective optimization with fitness landscap...

引用

Congress on Evolutionary Computation

作者： Shen Wang Mahdi Mahfouf Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper reports on an efficient algorithm for locating the `optimal' solutions for multi-objective optimization problems by combining a state-of-the-art optimizer with a fitness model-estimate. This hybrid framework is introduced to illustrate how to make sufficient use of an approximate model, which includes a `controlled' process and an `uncontrolled' process during the search. With the inclusion of such approximate model in the optimization block, a global reseeding strategy based on previous data is also applied to improve the ability of the multi-objective optimizer to find global set of solutions (`pareto' solutions). To this effect, the popular algorithm, NSGA-II, and a Multi-Layer Perceptron Neural Network (MLP) are combined synergetically to show details of such processing. Furthermore, a simple (but no simpler) method for selecting the `training' data necessary for eliciting the fitness landscape model is suggested to address what are now a common engineering problems, in particular those associated with sparse data distributions and objectives converging at significantly different speeds. To test the validity of the proposed multi-objective scheme, a series of simulation experiments, using well-know benchmark functions, are conducted and are compared to those carried-out while using the original NSGA-II and SPEA-2, under similar conditions. The proposed method is also applied to the `optimal' design of alloy steels in terms of chemical compositions and processing conditions and is shown to perform very well.

关键词： Optimization Training data Approximation methods Approximation algorithms Biological system modeling Training Predictive models

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ANFIS modelling of a twin rotor system using particle swarm optimisation and RLS

ANFIS modelling of a twin rotor system using particle swarm ...

引用

IEEE International Conference on Cybernetic Intelligent systems, CIS

作者： S. F. Toha M. O. Tokhi Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Artificial intelligence techniques, such as neural networks and fuzzy logic have shown promising results for modelling of nonlinear systems whilst traditional approaches are rather insufficient due to difficulty in modelling of highly nonlinear components in the system. A laboratory set-up that resembles the behaviour of a helicopter, namely twin rotor multi-input multi-output system (TRMS) is used as an experimental rig in this research. An adaptive neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm is developed in search for non-parametric model for the TRMS. The antecedent parameters of the ANFIS are optimized by a PSO algorithm and the consequent parameters are updated using recursive least squares (RLS). The results show that the proposed technique has better convergence and better performance in modeling of a nonlinear process. The identified model is justified and validated in both time domain and frequency domain.

关键词： Rotors Adaptation model Transmission line measurements Optimization Particle swarm optimization Artificial neural networks Helicopters

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Initial validation of a reverse osmosis simulator

Initial validation of a reverse osmosis simulator

引用

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luis Palacin Fernando Tadeo Johanna Salazar Cesar de Prada Department of Systems Engineering and Automatic Control University of Valladolid Valladolid Spain

Reverse Osmosis (RO) is the most common technique to produce drinkable water in arid regions. The desalination plants are usually designed for a short number of constant operation points. A dynamic strategy of control would help to decrease the operation expense and the equipments cost. However, the lack of dynamic simulation tools for this kind of plants, avoids the optimal design, which would use a continuously changeable operation point. For this, a new dynamic simulation library of RO plants was presented elsewhere. Now, the present paper deals with the validation of that library, using experimental data from a real RO plant.

关键词： Mathematical model Biological system modeling Libraries Desalination Biomembranes Object oriented modeling Reverse osmosis

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Active vibration control of a flexible plate structure using particle swarm optimization

Active vibration control of a flexible plate structure using...

引用

IEEE International Conference on Cybernetic Intelligent systems, CIS

作者： S. Julai M. O. Tokhi M. Mohamad I. Abd. Latiff Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

This paper presents investigations of modeling the flexible plate structures using particle swarm optimization (PSO) and active vibration control (AVC) of such structures. The optimization technique is utilized to obtain a dynamic model of a flexible plate structure based on auto-regressive with exogenous (ARX) input structure. The structure is subjected to two different disturbance signal types, namely random, and finite duration step. The fitness function for the PSO is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed PSO modeling approach is used for AVC system design to suppress the vibration of the flexible plate. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density (PSD) of the observed signal.

关键词： Mathematical model Optimization Frequency measurement automatic voltage control Vibrations Density measurement Attenuation

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‘Symbiotic’ data-driven modelling for the accurate prediction of mechanical properties of alloy steels

‘Symbiotic’ data-driven modelling for the accurate predict...

引用

International IEEE Conference on Intelligent systems, IS

作者： S. Gaffour M. Mahfouf Y.Y Yang Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

A new optimal strategy based on symbiotic modelling is proposed. The system combines Linear Regression Model (LR), Non-Linear Iterative Partial Adaptive Least Square Model (NIPALS), Neural Network Model with double loop procedures (NNDLP), Adaptive Numeric Modelling (Neural-Fuzzy modeling NF) and metallurgical knowledge in order to provide effective modelling solutions and achieve an optimal prediction performance. As a final step a fusion procedure is used to perform a routine decision making based on aggregation algorithm and clustering method that allow to systematically select the final best prediction outcome from a set of competing solutions. The proposed methodology is then applied to the challenging environment of a multi-dimensional, non-linear and sparse data space consisting of mechanical properties of `Mild' Steel in particular Tensile Strength (TS) and Yield Strength (YS) in hot-rolling industrial processes. Using a data set containing critical information on the mechanical properties obtained from a hot strip mill, it is concluded that the developed new systematic modelling approach is capable of providing better prediction than each individual model even in data distribution areas which are reckoned to be sparse.

关键词： Predictive models Mechanical factors Iron alloys Steel Numerical models Symbiosis Linear regression Least squares methods Neural networks Noise measurement

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Optimisation of metal microstructure using a Population Adaptive based Immune Algorithm (PAIA)

Optimisation of metal microstructure using a Population Adap...

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Mediterranean Conference on control and Automation (MED)

作者： Sidahmed Gaffour Mahdi Mahfouf Jun Chen Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

A new optimal design method and a systematic scheduling approach for a laboratory-scale Hot-Rolling Mill are presented. The proposed design is based upon 1. metallurgical principles, which sufficiently consider the behaviour of workpiece material and the mechanics of the manufacturing process and 2. a modified version for multi-objective optimization of a Population Adaptive based Immune Algorithm (PAIA), physically-based models and symbiotic modelling approach to carry-out an optimal search for the best microstructural parameters. This methodology possesses adequate capabilities for finding effective and best microstructural parameters such as the ferrite grain size and the volume fraction of pearlite that satisfy the requirements for mechanical properties. Hence, the overarching aim of this research work is to integrate knowledge about both the stock and the rolling process to find optimal hot-deformation profiles that will be used as information in order to compute the most suitable rolling schedule and systemise the optimal route for processing and achieve a `right-first-time' production of the desired properties.

关键词： Optimization Microstructure Adaptation model Mechanical factors Modeling Steel

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Experimental verification of accelerated norm-optimal iterative learning control

Experimental verification of accelerated norm-optimal iterat...

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UKACC International Conference on control 2010

作者： Chu, Bing Cai, Zhonglun Owens, David H. Rogers, Eric Freeman, Chris T. Lewin, Paul L. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD United Kingdom School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom

ISBN: (纸本)9781846000386

Accelerated Norm-Optimal Iterative Learning control (NOILC) is a recently developed method to improve the convergence performance of the well known NOILC algorithm. This paper investigates the effectiveness of this method experimentally on a gantry robot facility, which has been extensively used to test a wide range of linear model based ILC algorithms. The results obtained confirm that the accelerated algorithm outperforms NOILC algorithm and in particular, the improvements at initial stage can be substantial, which is of great interest in practical applications.

关键词： Iterative methods

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Integrated curriculum for control and systems engineering

Integrated curriculum for control and systems engineering

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UKACC International Conference on control 2010

作者： J. A. Rossiter L. Gray Department of Automatic Control and Systems Engineering University of Sheffield Yorks UK

ISBN: (纸本)9781627481199

This paper proposes and gives evidence supporting a team based approach to the design and implementation of engineering curricula. It shows how such an approach allows the effective integration of many different learning outcomes to give a coherent student experience. Finally, it uses examples to illustrate where the web and other technology can be invaluable tools for the delivery of a holistic curriculcum.

关键词： Integrated curriculum Independent learning Team work Integrated curriculum systems engineering and theory systems engineering Web search engines Student Experience Standing Orders Obstetric Delivery Curriculum

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Lymphocyte segmentation using the transferable belief model

Lymphocyte segmentation using the transferable belief model

引用

20th International Conference on Pattern Recognition, ICPR 2010

作者： Panagiotakis, Costas Ramasso, Emmanuel Tziritas, Georgios Department of Computer Science University of Crete P.O. Box 2208 Heraklion Greece FEMTO-ST Institute UMR CNRS 6174 - UFC/ENSMM/UTBM Automatic Control and Micro-Mechatronic Systems Department France

ISBN: (纸本)3642177107

In the context of several pathologies, the presence of lymphocytes has been correlated with disease outcome. The ability to automatically detect lymphocyte nuclei on histopathology imagery could potentially result in the development of an image based prognostic tool. In this paper we present a method based on the estimation of a mixture of Gaussians for determining the probability distribution of the principal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, a Transferable Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccentricity constraint principle. © 2010 Springer-Verlag.

关键词： Lymphocytes

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Progressive diversity management in evolutionary multiobjective optimisation

Progressive diversity management in evolutionary multiobject...

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Congress on Evolutionary Computation

作者： Salem F. Adra Peter J. Fleming Computer Science Department University of Sheffield Sheffield UK Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK

Convergence towards, and diversity across the Pareto-optimal front are the two main requirements when optimising a multiobjective optimisation problem (MOP) with conflicting objectives. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimisation, where the number of objectives is greater than 2 or 3, it has been found that these two requirements can conflict with one another, introducing problems such as dominance resistance and speciation. In this study, a previously introduced diversity management mechanism is deployed within a Progressive Preference Articulation (PPA) technique to optimise an 8-objective real-world problem of aircraft control system design. This paper illustrates the effective application of the new diversity management mechanism used in conjunction with the PPA technique when optimising a multiobjective real-world engineering problem.

关键词： Optimization Mathematical model Convergence Equations Measurement Aircraft Delta modulation

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