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A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

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evolutionary INTELLIGENCE 2010年第3-4期3卷 137-153页

作者： Marcelloni, Francesco Vecchio, Massimo Univ Pisa Dipartimento Ingn Informaz Via Diotisalvi 2 I-56122 Pisa Italy Univ Vigo Signal Theory & Commun Dept Vigo Spain

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-objective evolutionary 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.

关键词： Wireless sensor networks Data compression multi-objective evolutionary algorithms Energy efficiency Signal processing

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A multi-objective Neuro-evolutionary Algorithm to Obtain Interpretable Fuzzy Models

A Multi-objective Neuro-evolutionary Algorithm to Obtain Int...

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13th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2009)

作者： Sanchez, Gracia Jimenez, Fernando Sanchez, Jose F. Alcaraz, Jose M. Univ Murcia Dept Ingn Informac & Comunicac E-30001 Murcia Spain

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-objective evolutionary 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.

关键词： TSK Fuzzy Models multi-objective evolutionary algorithms RBF Neural Networks

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multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets

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SOFT COMPUTING 2010年第7期14卷 713-728页

作者： Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

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-objective evolutionary 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.

关键词： Genetic fuzzy rule-based classifiers multi-objective evolutionary algorithms Imbalanced datasets ROC curves Convex hull method

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Insights on surface wave dispersion and HVSR: Joint analysis via Pareto optimality

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JOURNAL OF APPLIED GEOPHYSICS 2010年第2期72卷 129-140页

作者： Giancarlo, Dal Moro Rhein Westfal TH Aachen GUtech Aachen Germany

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 (evolutionary algorithms) 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.

关键词： Joint inversion Surface wave dispersion HVSR Shear-wave velocity multi-objective evolutionary algorithms Rayleigh waves

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One decade of multi-objective calibration approaches in hydrological modelling: a review

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2010年第1期55卷 58-78页

作者： Efstratiadis, Andreas Koutsoyiannis, Demetris Natl Tech Univ Athens Sch Civil Engn Dept Water Resources & Environm Engn GR-15780 Zografos Greece

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.

关键词： multi-objective evolutionary algorithms multiple responses uncertainty equifinality hybrid calibration soft data

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Set-based concept selection in multi-objective problems involving delayed decisions

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JOURNAL OF ENGINEERING DESIGN 2010年第6期21卷 619-646页

作者： Avigad, Gideon Moshaiov, Amiram ORT Braude Coll Engn Dept Mech Engn Karmiel Israel Tel Aviv Univ Dept Mech Engn IL-69978 Tel Aviv Israel

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.

关键词： multi-criteria decision-making multi-objective evolutionary algorithms conceptual design uncertainties robustness design space representation

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multi-objective NETWORK RELIABILITY OPTIMIZATION USING evolutionary algorithms

MULTI-OBJECTIVE NETWORK RELIABILITY OPTIMIZATION USING EVOLU...

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15th ISSAT International Conference on Reliability and Quality in Design

作者： Aguirre, Oswaldo Villanueva, Delia Taboada, Heidi Univ Texas El Paso Dept Ind Engn El Paso TX 79968 USA

ISBN: (纸本)9780976348658

In the present paper a multi-objective evolutionary 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 objective evolutionary algorithm was developed. Examples are presented for each of the different problems to show the performance of the algorithm.

关键词： All-terminal k-terminal two-terminal network reliability multi-objective evolutionary algorithms

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COMPARISON OF COMPUTATIONAL EFFICIENCY OF MOEA\D AND NSGA-II FOR PASSIVE VEHICLE SUSPENSION OPTIMIZATION

COMPARISON OF COMPUTATIONAL EFFICIENCY OF MOEA\D AND NSGA-II...

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24th European Conference on Modelling and Simulation

作者： Yuen, Tey Jing Ramli, Rahizar Univ Malaya Dept Mech Engn Kuala Lumpur 50603 Malaysia

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.

关键词： Passive suspension optimization multi-objective evolutionary algorithms genetic algorithms quarter vehicle model ride comfort

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Visualization of multi-objective Design Space Exploration for Embedded Systems

Visualization of Multi-Objective Design Space Exploration fo...

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13th Euromicro Conference on Digital System Design on Architectures, Methods and Tools

作者： Taghavi, Toktam Pimentel, Andy D. Univ Amsterdam Inst Informat Comp Syst Architecture Grp Amsterdam Netherlands

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.

关键词： component design space exploration visualization multi-objective evolutionary algorithms embedded systems

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Stochastic optimization of a neural network-based controller for aggressive maneuvers on loose surfaces

Stochastic optimization of a neural network-based controller...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Terekhov, Alexander V. Mouret, Jean-Baptiste Grand, Christophe UPMC CNRS Inst Syst Intelligents & Robot F-75005 Paris France

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.

关键词： aggressive maneuvers closed loop systems feedback feedback controller mobile robots multi-objective evolutionary algorithms neural network based controller neurocontrollers nonlinear control nonlinear control systems optimisation stochastic optimization stochastic processes wheeled autonomous mobile robot

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