This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) ...
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This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km(2)), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a bayesian optimization algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms;in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose thre
Assessing the reliability of safety-critical systems is an important and challenging task because even a minor failure in these systems may result in catastrophic consequences, like losing human life. A well-known and...
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Assessing the reliability of safety-critical systems is an important and challenging task because even a minor failure in these systems may result in catastrophic consequences, like losing human life. A well-known and fully automatic technique in reliability assessing approaches is model checking. However, applying this technique to verify some properties such as safety may lead to the state space explosion problem in which all reachable states cannot be checked due to computational limitations. In such situations that the verification of a safety property is infeasible, it is possible to refute the safety property by searching a reachable state in which a special configuration (e.g., an error or an undesirable behaviour) occurs. Therefore, checking reachability can be done instead of refuting the corresponding safety property. Finding such reachable states, in the worst case, may cause the state space explosion problem again. Hence, using evolutionary algorithms to explore the state space efficiently can be a promising idea. In this paper, at first, we propose an evolutionary algorithm to check reachability properties and refute safety ones in software systems specified formally through graph transformations. Since the accuracy and convergence speed of the proposed approach can still be improved, we employ the bayesian optimization algorithm (BOA) to propose another approach. In BOA, a bayesian network is learnt from the population and then sampled to generate new solutions. The proposed approaches can be used to analyse the reachability and safety properties. The proposed approaches are implemented in GROOVE which is an open source toolset for designing and model checking graph transformation systems. To evaluate the efficiency of the proposed approaches, different benchmark problems are employed. Experimental results show that the proposed approaches are faster and more accurate than the existing methods.
Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorith...
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Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on the bayesian optimization algorithm (BOA) for heterogeneous computing environments. In the proposed algorithm, scheduling is divided into two phases. First, according to the task graph of multiprocessor scheduling problems, bayesian networks are initialized and learned to capture the dependencies between different tasks. And the promising solutions assigning tasks to different processors are generated by sampling the bayesian network. Second, the execution sequence of tasks on the same processor is set by the heuristic-based priority used in the list scheduling approach. The proposed algorithm is evaluated and compared with the related approaches by means of the empirical studies on random task graphs and benchmark applications. The experimental results show that the proposed algorithm is able to deliver more efficient schedules. Further experiments indicate that the proposed algorithm maintains almost the same performance with different parameter settings. (C) 2010 Elsevier B.V. All rights reserved.
Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by ...
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Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem. However, as the complexity of the used models increases, the chance of overfitting and consequently reducing model interpretability, increases as well. This paper investigates the relationship between the probabilistic models learned by the bayesian optimization algorithm (BOA) and the underlying problem structure. The purpose of the paper is threefold. First, model building in BOA is analyzed to understand how the problem structure is learned. Second, it is shown how the selection operator can lead to model overfitting in bayesian EDAs. Third, the scoring metric that guides the search for an adequate model structure is modified to take into account the non-uniform distribution of the mating pool generated by tournament selection. Overall, this paper makes a contribution towards understanding and improving model accuracy in BOA, providing more interpretable models to assist efficiency enhancement techniques and human researchers.
Studies show that application of the prior knowledge in biasing the Estimation of Distribution algorithms (EDAs), such as bayesian optimization algorithm (BOA), increases the efficiency of these algorithms significant...
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Studies show that application of the prior knowledge in biasing the Estimation of Distribution algorithms (EDAs), such as bayesian optimization algorithm (BOA), increases the efficiency of these algorithms significantly. One of the main advantages of the EDAs over other optimizationalgorithms is that the former provides a trail of probabilistic models of candidate solutions with increasing quality. Some recent studies have applied these probabilistic models, obtained from previously solved problems in biasing the BOA algorithm, to solve the future problems. In this paper, in order to improve the previous works and reduce their disadvantages, a method based on Case Based Reasoning (CBR) is proposed for biasing the BOA algorithm. Herein, after running BOA for solving optimization problems, each problem, the corresponding solution, as well as the last bayesian network obtained from the BOA algorithm, will be stored as an entry in the case-base. Upon introducing a new problem, similar problems from the case-base are retrieved and the last bayesian networks of these solved problems are combined according to the degree of their similarity with the new problem;hence, a compound bayesian network is constructed. The compound bayesian network is sampled and the initial population for the BOA algorithm is generated. This network will be applied efficiently for biasing future probabilistic models during the runs of BOA for the new problem. The proposed method is tested on three well-known combinatorial benchmark problems. Experimental results show significant improvements in algorithm execution time and quality of solutions, compared to previous methods. (C) 2011 Elsevier B.V. All rights reserved.
Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. D...
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Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. Due to it according with the real production system, we adopt a hybrid evolutionary computation algorithm to solve the fJSP problems. Among them, the bayesian optimization algorithm (BOA) is introduced to the characteristics of scheduling and uncertainty characteristics of the time in the fJSP. On this basis, we propose a distributed evolutionary algorithm and parameter adaptive mechanism. Finally, through experiments, we conclude that the proposed hybrid evolutionary algorithm based on BOA with grouping mechanism get better solution than original algorithm and improve robustness of algorithm. Meanwhile, the paper also have objective perspective, that is we can group the data different from each other, make the whole population into sub-populations, and then make the experiment separately on different and parallel machines in distributed environment, so that not only optimizes the best solution, but also enhance the efficiency and shortened the time. (C) 2015 The Authors. Published by Elsevier B.V.
The electromagnetic interference (EMI) problem of extra-high speed electronic devices and systems is becoming more complex with an increase of operating frequency. The conventional analysis and design methods could no...
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ISBN:
(纸本)9781538612392;9781538612385
The electromagnetic interference (EMI) problem of extra-high speed electronic devices and systems is becoming more complex with an increase of operating frequency. The conventional analysis and design methods could not cope with the current EMI problems. Advanced analysis and design methods are desired. Deep neural network (DNN) and bayesian optimization algorithm (BOA) based on machine learning are utilized in prediction of EMI radiation, optimization of design parameters and localization of EMI sources. The feasibility of DNN and BOA is investigated and validated. The steps of using DNN and BOA are proposed in the paper.
Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. D...
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Flexible Job-shop Scheduling Problem (fJSP) is a typical and important scheduling problem in Flexible Manufacturing System (FMS). The fJSP is an extended version of Job-shop Scheduling (JSP) that is NP hard problem. Due to it according with the real production system, we adopt a hybrid evolutionary computation algorithm to solve the fJSP problems. Among them, the bayesian optimization algorithm (BOA) is introduced to the characteristics of scheduling and uncertainty characteristics of the time in the fJSP. On this basis, we propose a distributed evolutionary algorithm and parameter adaptive mechanism. Finally, through experiments, we conclude that the proposed hybrid evolutionary algorithm based on BOA with grouping mechanism get better solution than original algorithm and improve robustness of algorithm. Meanwhile, the paper also have objective perspective, that is we can group the data different from each other, make the whole population into sub-populations, and then make the experiment separately on different and parallel machines in distributed environment, so that not only optimizes the best solution, but also enhance the efficiency and shortened the time.
In DBOA, to build accurately the best bayesian network with respect to most metrics is NP-complete and the high time complexity of learning the model structure becomes a bottleneck of DBOA for real application. Conseq...
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ISBN:
(纸本)9789811003561;9789811003554
In DBOA, to build accurately the best bayesian network with respect to most metrics is NP-complete and the high time complexity of learning the model structure becomes a bottleneck of DBOA for real application. Consequently, in order to decrease the asymptotic time complexity of model building and make the algorithm more practical even for extremely large and complex problem, this paper presents adaptive sporadic model building based on estimation of model similarity as an efficiency enhancement technique of DBOA. The results show that performing the adaptive model building in DBOA can reduce the number of building model under no increasing on the number of generation and population size necessary to converge to optimal solutions, and achieve a better trade-off between the convergence speed and convergence results.
The evolutions computation is the best proceeding algorithm for all kinds of optimization problem In the world. bayesian optimization algorithm (BOA) is one kind of the evolution algorithm which is advantage on others...
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ISBN:
(纸本)9780769538167
The evolutions computation is the best proceeding algorithm for all kinds of optimization problem In the world. bayesian optimization algorithm (BOA) is one kind of the evolution algorithm which is advantage on others for high order, hierarchical and correlative on anther optimization problem. For improving the ability of the BOA, the decision graph was introduced to enhance the represent and learn of bayesian network and compress the parameter saving. The optimization mechanism and the algorithm model were studied in detail. The evaluation indexes and test function were also built for validating the merits. The performance and efficiency of DBOA were analyzed with compare with BOA, basic genetic algorithm and binary particle swarm optimization. The test results showed that DBOA was effective for hierarchical decomposable function.
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