Since tiny nodes of a wireless sensor network (WSN) are typically powered by batteries with, due to miniaturization and costs, a limited capacity, with the aim of extending the lifetime of WSNs and making the exploita...
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Since tiny nodes of a wireless sensor network (WSN) are typically powered by batteries with, due to miniaturization and costs, a limited capacity, with the aim of extending the lifetime of WSNs and making the exploitation of WSNs appealing, a lot of research has been devoted to save energy. Although a number of factors contribute to power consumption, radio communication has been generally considered its main cause and thus most of the techniques proposed for energy saving have mainly focused on limiting transmission/reception of data, for instance, through data compression. As sensor nodes are equipped with limited computational and storage resources, enabling compression requires to develop purposely-designed algorithms. To this aim, we propose an approach to generate lossy compressors to be deployed on single nodes based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. The quantization levels and thresholds, which allow achieving different trade-offs between compression performance and information loss, are determined by a two-objectiveevolutionary algorithm. We tested our approach on four datasets collected by real WSN deployments. We show that the lossy compressors generated by our approach can achieve significant compression ratios despite negligible reconstruction errors and outperform LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes.
In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. A multi-objective evolu...
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ISBN:
(纸本)9783642142635
In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. A multi-objectiveevolutionary algorithm is implemented with three different selection and generational replacements schemata (Niched Preselection, NSGA-II and ENORA) to generate fuzzy models in the proposed optimization context. The results clearly show a real ability and effectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models. These schemata have been compared in terms of accuracy, interpretability and compactness by using three test problems studied in literature. Statistical tests have also been used with optimality and diversity multi-objective metrics to compare the schemata.
We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms...
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We exploit an evolutionary three-objective optimization algorithm to produce a Pareto front approximation composed of fuzzy rule-based classifiers (FRBCs) with different trade-offs between accuracy (expressed in terms of sensitivity and specificity) and complexity (computed as sum of the conditions in the antecedents of the classifier rules). Then, we use the ROC convex hull method to select the potentially optimal classifiers in the projection of the Pareto front approximation onto the ROC plane. Our method was tested on 13 highly imbalanced datasets and compared with 2 two-objectiveevolutionary approaches and one heuristic approach to FRBC generation, and with three well-known classifiers. We show by the Wilcoxon signed-rank test that our three-objective optimization approach outperforms all the other techniques, except for one classifier, in terms of the area under the ROC convex hull, an accuracy measure used to globally compare different classification approaches. Further, all the FRBCs in the ROC convex hull are characterized by a low value of complexity. Finally, we discuss how, the misclassification costs and the class distributions are fixed, we can select the most suitable classifier for the specific application. We show that the FRBC selected from the convex hull produced by our three-objective optimization approach achieves the lowest classification cost among the techniques used as comparison in two specific medical applications.
Surface Wave (SW) dispersion and Horizontal-to-Vertical Spectral Ratio (HVSR) are known as tools able to provide possibly complementary information useful to depict the vertical shear-wave velocity profile. Their join...
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Surface Wave (SW) dispersion and Horizontal-to-Vertical Spectral Ratio (HVSR) are known as tools able to provide possibly complementary information useful to depict the vertical shear-wave velocity profile. Their joint analysis might then be able to overcome the limits which inevitably affect such methodologies when they are singularly considered. When a problem involves the optimization (i.e. the inversion) of two or more objectives, the standard practice is represented by a normalized summation able to account for the typically different nature and magnitude of the considered phenomena (thus objective functions). This way, a single cost function is obtained and the optimization problem is performed through standard solvers. This approach is often problematic not only because of the mathematically and physically inelegant summation of quantities with different magnitudes and units of measurements. The critical point is indeed represented by the inaccurate performances necessarily obtained while dealing with problems characterized by several local minima and the impossibility of a rigorous assessment of the goodness and meaning of the final result. In the present paper joint analysis of both synthetic and field SW dispersion curves and HVSR datasets is performed via the Pareto front analysis. Results show the relevance of Pareto's criterion not only as ranking system to proceed in heuristic optimization (evolutionaryalgorithms) but also as a tool able to provide some insights about the characteristics of the analyzed signals and the overall congruency of data interpretation and inversion. Possible asymmetry of the final Pareto front models is discussed in the light of relative non-uniqueness of the two considered objective functions. (C) 2010 Elsevier B.V. All rights reserved.
One decade after the first publications on multi-objective calibration of hydrological models, we summarize the experience gained so far by underlining the key perspectives offered by such approaches to improve parame...
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One decade after the first publications on multi-objective calibration of hydrological models, we summarize the experience gained so far by underlining the key perspectives offered by such approaches to improve parameter identification. After reviewing the fundamentals of vector optimization theory and the algorithmic issues, we link the multi-criteria calibration approach with the concepts of uncertainty and equifinality. Specifically, the multi-criteria framework enables recognition and handling of errors and uncertainties, and detection of prominent behavioural solutions with acceptable trade-offs. Particularly in models of complex parameterization, a multi-objective approach becomes essential for improving the identifiability of parameters and augmenting the information contained in calibration by means of both multi-response measurements and empirical metrics ("soft" data), which account for the hydrological expertise. Based on the literature review, we also provide alternative techniques for dealing with conflicting and non-commeasurable criteria, and hybrid strategies to utilize the information gained towards identifying promising compromise solutions that ensure consistent and reliable calibrations.
This paper introduces a computational approach to support concept selection in multi-objective design. It is motivated by: (1) a common need to delay some decisions during conceptual design due to the presence of unce...
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This paper introduces a computational approach to support concept selection in multi-objective design. It is motivated by: (1) a common need to delay some decisions during conceptual design due to the presence of uncertainties;and (2) intentional delay of decisions for the purpose of maintaining several optional concepts, as suggested by the concurrent engineering procedure of Toyota. Here, for the first time, a multi-objective set-based concept (SBC) selection problem with delayed decisions is formulated and solved. SBCs are conceptual solutions, which are represented by sets of particular solutions, with each concept having a one-to-many relation with the objective space. Several novel notions, such as higher-level concepts, multi-model concepts and robust concepts to delayed decisions, are defined and used. These lead to an auxiliary multi-objective decision problem. The auxiliary objectives are concept optimality and variability, both paramount to concept selection, with concept variability strongly supporting the idea of intentionally keeping several useful alternatives as long as possible. Academic and engineering examples are provided to demonstrate the proposed approach and its applicability to real-life problems. The results demonstrate that the suggested technique may well support the process of delayed decision either when needed or when deliberately done.
In the present paper a multi-objectiveevolutionary algorithm is developed to solve three different network reliability design problems taking in account reliability, cost and weight as objective functions to be optim...
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ISBN:
(纸本)9780976348658
In the present paper a multi-objectiveevolutionary algorithm is developed to solve three different network reliability design problems taking in account reliability, cost and weight as objective functions to be optimized simultaneously. The solution presented for each of the different problems is a set of Pareto-optimal solutions. The three problems considered are the all-terminal, the k-terminal, and the two-terminal network reliability problems. To solve these well-known network reliability problems, a multiple objectiveevolutionary algorithm was developed. Examples are presented for each of the different problems to show the performance of the algorithm.
This paper evaluates new optimization algorithms for optimizing automotive suspension systems employing stochastic methods. This method is introduced as an alternative over the conventional approach, namely trial and ...
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ISBN:
(纸本)9780956494405
This paper evaluates new optimization algorithms for optimizing automotive suspension systems employing stochastic methods. This method is introduced as an alternative over the conventional approach, namely trial and error, or design of experiment (DOE), to efficiently optimize the suspension system. Optimizations algorithms employed are the multi-objective evolutionary algorithms based on decomposition (MOEA\D), and non-sorting genetic algorithm II (NSGA-II). A two-degree-of-freedom (2- DOF) linear quarter vehicle model (QVM) traversing a random road profile is utilized to describe the ride dynamics. The road irregularity is assumed as a Gaussian random process and represented as a simple exponential power spectral density (PSD). The evaluated performance indices are the discomfort parameter (ACC), suspension working space (SWS) and dynamic tyre load (DTL). The optimised design variables are the suspension stiffness, K-s and damping coefficient, C-s. In this paper, both algorithms are analyzed with different sets of experiments to compare their computational efficiency. The results indicated that MOEA\D is computationally efficient in searching for Pareto solutions compared to NSGA-II, and showed reasonable improvement in ride comfort.
Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other ha...
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ISBN:
(纸本)9780769541716
Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other hand, they need to achieve high (real-time) performance. This wide spectrum of design requirements leads to complex heterogeneous system-on chip (SoC) architectures. The complexity of embedded systems forces designers to model and simulate systems and their components to explore the wide range of design choices. Such design space exploration is especially needed during the early design stages, where the design space is at its largest. Due to the exponential design space in real problems and multiple criteria to be considered, multi-objective evolutionary algorithms (MOEAs) are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. Interpreting the search results (e.g., where are the Pareto points located), understanding their relations and analyzing how the design space was searched by such searching algorithms is of invaluable importance to the designer. To this end, this paper presents a novel interactive visualization tool, based on tree visualization, to understand the search dynamics of a MOEA and to visualize where the optimum design points are located in the design space and what objective values they have.
In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity ...
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ISBN:
(纸本)9781424466757
In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly non-linear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.
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