Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557775
R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo
The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.
{"title":"A Genetic Programming based approach to automatically generate Wireless Sensor Networks applications","authors":"R. R. Oliveira, T. Heimfarth, R. W. Bettio, M. Arantes, C. Toledo","doi":"10.1109/CEC.2013.6557775","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557775","url":null,"abstract":"The development of Wireless Sensor Networks (WSNs) applications is an arduous task, since the application needs to be customized for each sensor. Thus, the automatic generation of WSN's applications is desirable to reduce costs, since it drastically reduces the human effort. This paper presents the use of Genetic Programming to automatically generate WSNs applications. A scripting language based on events and actions is proposed to represent the WSN behavior. Events represent the state of a given sensor node and actions modify these states. Some events are internal states and others are external states captured by the sensors. The genetic programming is used to automatically generate WSNs applications described using this scripting language. These scripts are executed by all network's sensors. This approach enables the application designer to define only the overall objective of the WSN. This objective is defined by means of a fitness function. An event-detection problem is presented in order to evaluate the proposed method. The results shown the capability of the developed approach to successfully solve WSNs problems through the automatic generation of applications.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131797121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557718
A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem
Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.
{"title":"Evolutionary medical image registration using automatic parameter tuning","authors":"A. Valsecchi, Jérémie Dubois-Lacoste, T. Stützle, S. Damas, J. Santamaría, L. Marrakchi-Kacem","doi":"10.1109/CEC.2013.6557718","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557718","url":null,"abstract":"Image registration is a fundamental step in combining information from multiple images in medical imaging, computer vision and image processing. In this paper, we configure a recent evolutionary algorithm for medical image registration, r-GA, with an offline automatic parameter tuning technique. In addition, we demonstrate the use of automatic tuning to compare different registration algorithms, since it allows to consider results that are not affected by the ability and efforts invested by the designers in configuring the different algorithms, a crucial task that strongly impacts their performance. Our experimental study is carried out on a large dataset of brain MRI, on which we compare the performance of r-GA with four classic IR techniques. Our results show that all algorithms benefit from the automatic tuning process and indicate that r-GA performs significantly better than the competitors.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557649
Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir
This paper investigates the influence of measures of good and bad diversity when used explicitly to guide the search of a genetic algorithm to design ensemble systems. We then analyze what the best set of objectives between classification error, good diversity and bad diversity as well as all combination of them. In this analysis, we make use of the NSGA II algorithm in order to generate ensemble systems, using k-NN as individual classifiers and majority vote as the combination method. The main goal of this investigation is to determine which set of objectives generates more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversity) have a positive effect in the construction of ensembles and if they can replace the classification error as optimization objective without causing losses in the accuracy level of the generated ensembles.
{"title":"Using good and bad diversity measures in the design of ensemble systems: A genetic algorithm approach","authors":"Antonino Feitosa Neto, A. Canuto, Teresa B Ludermir","doi":"10.1109/CEC.2013.6557649","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557649","url":null,"abstract":"This paper investigates the influence of measures of good and bad diversity when used explicitly to guide the search of a genetic algorithm to design ensemble systems. We then analyze what the best set of objectives between classification error, good diversity and bad diversity as well as all combination of them. In this analysis, we make use of the NSGA II algorithm in order to generate ensemble systems, using k-NN as individual classifiers and majority vote as the combination method. The main goal of this investigation is to determine which set of objectives generates more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversity) have a positive effect in the construction of ensembles and if they can replace the classification error as optimization objective without causing losses in the accuracy level of the generated ensembles.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557930
R. Prati, F. O. França
In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.
{"title":"Extending features for multilabel classification with swarm biclustering","authors":"R. Prati, F. O. França","doi":"10.1109/CEC.2013.6557930","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557930","url":null,"abstract":"In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557782
Florian Siegmund, A. Ng, K. Deb
In Evolutionary Multi-objective Optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based Optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) [9]. The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions.
{"title":"A comparative study of dynamic resampling strategies for guided Evolutionary Multi-objective Optimization","authors":"Florian Siegmund, A. Ng, K. Deb","doi":"10.1109/CEC.2013.6557782","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557782","url":null,"abstract":"In Evolutionary Multi-objective Optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based Optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) [9]. The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122994120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557625
Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell
This paper explores the potential for transistor level fault tolerance on a new Programmable Analogue and Digital Array (PAnDA) architecture1. In particular, this architecture features Combinatorial Configurable Analogue Blocks (CCABs) that can implement a number of combinatorial functions similar to FPGAs. In addition, PAnDA allows one to reconfigure features of the underlying analogue layer. In PAnDA-EINS, the functions that the CCAB can implement are predefined through the use of a routing block. This paper is a study of whether removing this routing block and allowing direct control of the transistors provides benefits for fault tolerance. Experiments are conducted in two stages. In the first stage, a logic function is evolved on a CCAB and then optimised using a GA. A fault is then injected into the substrate, breaking the logic function. The second stage of the experiment consists of evolving the logic function again on the faulty substrate. The results of these experiments show that the removal of the routing block from the CCAB is beneficial for fault tolerance.
{"title":"Overcoming faults using evolution on the PAnDA architecture","authors":"Pedro B. Campos, David M. R. Lawson, S. Bale, James Alfred Walker, M. Trefzer, A. Tyrrell","doi":"10.1109/CEC.2013.6557625","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557625","url":null,"abstract":"This paper explores the potential for transistor level fault tolerance on a new Programmable Analogue and Digital Array (PAnDA) architecture1. In particular, this architecture features Combinatorial Configurable Analogue Blocks (CCABs) that can implement a number of combinatorial functions similar to FPGAs. In addition, PAnDA allows one to reconfigure features of the underlying analogue layer. In PAnDA-EINS, the functions that the CCAB can implement are predefined through the use of a routing block. This paper is a study of whether removing this routing block and allowing direct control of the transistors provides benefits for fault tolerance. Experiments are conducted in two stages. In the first stage, a logic function is evolved on a CCAB and then optimised using a GA. A fault is then injected into the substrate, breaking the logic function. The second stage of the experiment consists of evolving the logic function again on the faulty substrate. The results of these experiments show that the removal of the routing block from the CCAB is beneficial for fault tolerance.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557659
D. Sharma, Prem Soren
Infeasibility driven approach is proposed in this paper for constrained bi-objective optimization using evolutionary algorithm. The idea is motivated from one of the constraint handling techniques in which infeasible solutions are preserved in the population for focusing the optimal solution lying on the boundary of feasible region. In the proposed approach, extreme solutions of the current non-dominated front are allowed to recombine only with extreme infeasible solutions. This restricted mating is expected to generate offspring towards the “Paretooptimal” front and reduces number of generations required to evolve comparative results against existing multi-objective evolutionary algorithm (MOEA). Although the proposed approach is generic and can be coupled with any MOEA, but for bench-marking purpose it is coupled with NSGA-II (refer as IDMOEA) and is tested on four engineering optimization problems. On an average for 30 different runs, IDMOEA shows quicker convergence than NSGA-II with equivalent quality of solutions assessed by indicator analysis.
{"title":"Infeasibility driven approach for bi-objective evolutionary optimization","authors":"D. Sharma, Prem Soren","doi":"10.1109/CEC.2013.6557659","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557659","url":null,"abstract":"Infeasibility driven approach is proposed in this paper for constrained bi-objective optimization using evolutionary algorithm. The idea is motivated from one of the constraint handling techniques in which infeasible solutions are preserved in the population for focusing the optimal solution lying on the boundary of feasible region. In the proposed approach, extreme solutions of the current non-dominated front are allowed to recombine only with extreme infeasible solutions. This restricted mating is expected to generate offspring towards the “Paretooptimal” front and reduces number of generations required to evolve comparative results against existing multi-objective evolutionary algorithm (MOEA). Although the proposed approach is generic and can be coupled with any MOEA, but for bench-marking purpose it is coupled with NSGA-II (refer as IDMOEA) and is tested on four engineering optimization problems. On an average for 30 different runs, IDMOEA shows quicker convergence than NSGA-II with equivalent quality of solutions assessed by indicator analysis.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125481972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557693
G. Papa, J. Silc
A parameter-less algorithm allows optimal solutions to be found without the need for setting the control parameters. Namely, finding an appropriate parameter setup for an evolutionary algorithm is a challenging research problem, and the setup optimality is crucial for algorithm's good performance. Therefore, the approaches that are able to solve any problem without any human intervention to set suitable control parameters are particulary interesting. The Parameterless Evolutionary Search (PLES) algorithm, with its real-value and combinatorial version, is based on a basic genetic algorithm, but it does not need any control parameter to be set in advance. It is able to find optimal, or at least very good, solutions relatively quickly, and without the need for a parameter-setting specialist. The last of these is a very important issue when used by engineers that do not have a detailed background knowledge: neither about optimization algorithms, nor about the settings of their control parameters. The efficiency of the proposed parameter-less algorithm was already evaluated using theoretical and real-world problems, being either real-valued or combinatorial. It was shown that the presented, adaptive, parameter-less algorithm has a faster convergence than comparable algorithms. Furthermore, it demonstrates its search ability by finding the solution without the need for predefined control parameters.
{"title":"The Parameter-less Evolutionary Search for real-parameter single objective optimization","authors":"G. Papa, J. Silc","doi":"10.1109/CEC.2013.6557693","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557693","url":null,"abstract":"A parameter-less algorithm allows optimal solutions to be found without the need for setting the control parameters. Namely, finding an appropriate parameter setup for an evolutionary algorithm is a challenging research problem, and the setup optimality is crucial for algorithm's good performance. Therefore, the approaches that are able to solve any problem without any human intervention to set suitable control parameters are particulary interesting. The Parameterless Evolutionary Search (PLES) algorithm, with its real-value and combinatorial version, is based on a basic genetic algorithm, but it does not need any control parameter to be set in advance. It is able to find optimal, or at least very good, solutions relatively quickly, and without the need for a parameter-setting specialist. The last of these is a very important issue when used by engineers that do not have a detailed background knowledge: neither about optimization algorithms, nor about the settings of their control parameters. The efficiency of the proposed parameter-less algorithm was already evaluated using theoretical and real-world problems, being either real-valued or combinatorial. It was shown that the presented, adaptive, parameter-less algorithm has a faster convergence than comparable algorithms. Furthermore, it demonstrates its search ability by finding the solution without the need for predefined control parameters.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134589085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557729
J. Leitner, Simon Harding, Mikhail Frank, A. Förster, J. Schmidhuber
Robust object manipulation is still a hard problem in robotics, even more so in high degree-of-freedom (DOF) humanoid robots. To improve performance a closer integration of visual and motor systems is needed. We herein present a novel method for a robot to learn robust detection of its own hands and fingers enabling sensorimotor coordination. It does so solely using its own camera images and does not require any external systems or markers. Our system based on Cartesian Genetic Programming (CGP) allows to evolve programs to perform this image segmentation task in real-time on the real hardware. We show results for a Nao and an iCub humanoid each detecting its own hands and fingers.
{"title":"Humanoid learns to detect its own hands","authors":"J. Leitner, Simon Harding, Mikhail Frank, A. Förster, J. Schmidhuber","doi":"10.1109/CEC.2013.6557729","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557729","url":null,"abstract":"Robust object manipulation is still a hard problem in robotics, even more so in high degree-of-freedom (DOF) humanoid robots. To improve performance a closer integration of visual and motor systems is needed. We herein present a novel method for a robot to learn robust detection of its own hands and fingers enabling sensorimotor coordination. It does so solely using its own camera images and does not require any external systems or markers. Our system based on Cartesian Genetic Programming (CGP) allows to evolve programs to perform this image segmentation task in real-time on the real hardware. We show results for a Nao and an iCub humanoid each detecting its own hands and fingers.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134560825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-06-20DOI: 10.1109/CEC.2013.6557736
E. Iuliano, D. Quagliarella
A surrogate-based optimization framework is proposed to exploit a reduced order model (ROM) as surrogate evaluator in aerodynamic design based on computational fluid dynamics (CFD) methods. The model is based on the Proper Orthogonal Decomposition (POD) of an ensemble of CFD solutions. Full POD and zonal POD models performances are analysed with respect to their suitability to find the global optimum in an evolutionary optimization frame. Indeed, reduced order models are used as fitness evaluator to improve the aerodynamic performances of a two-dimensional airfoil. Finally, the performances of various surrogate-based shape optimization (SBSO) methods are compared to the efficiency of data-fit assisted optimization and to the accuracy of a plain optimization, where, instead, each aerodynamic evaluation is performed with the high-fidelity model.
{"title":"Aerodynamic shape optimization via non-intrusive POD-based surrogate modelling","authors":"E. Iuliano, D. Quagliarella","doi":"10.1109/CEC.2013.6557736","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557736","url":null,"abstract":"A surrogate-based optimization framework is proposed to exploit a reduced order model (ROM) as surrogate evaluator in aerodynamic design based on computational fluid dynamics (CFD) methods. The model is based on the Proper Orthogonal Decomposition (POD) of an ensemble of CFD solutions. Full POD and zonal POD models performances are analysed with respect to their suitability to find the global optimum in an evolutionary optimization frame. Indeed, reduced order models are used as fitness evaluator to improve the aerodynamic performances of a two-dimensional airfoil. Finally, the performances of various surrogate-based shape optimization (SBSO) methods are compared to the efficiency of data-fit assisted optimization and to the accuracy of a plain optimization, where, instead, each aerodynamic evaluation is performed with the high-fidelity model.","PeriodicalId":211988,"journal":{"name":"2013 IEEE Congress on Evolutionary Computation","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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