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A Comparative Study of Eighteen self-adaptive Metaheuristic algorithms for Truss Sizing Optimisation

KSCE JOURNAL OF CIVIL ENGINEERING 2018年第8期22卷 2982-2993页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Sustainable Infrastruct Res & Dev Ctr Khon Kaen 40002 Thailand

Performance comparison of meta-heuristics (MHs) is conducted for truss sizing design. Six traditional truss sizing design problems with mass objective function subject to displacement and stress constraints were employed for performance test. The test problems have two types with and without including buckling constraints. Eighteen self-adaptive MHs from literature are employed to tackle the truss sizing problems. The results from implementing the self-adaptive MHs are compared in terms of convergence rate and consistency. It is found that for the test problem without buckling constraints, the top two optimisers according to the statistical Wilcoxon rank sum tests are Success-History Based adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) and Success-History Based adaptive Differential Evolution (SHADE) while the top two optimiser for the test problems with buckling constraints is L-SHADE and L-SHADE with Eigenvector-Based Crossover and Successful-Parent-Selecting Framework (SPS-L-SHADE-EIG). The buckling constraints are significantly important and should be included to truss design subjected to static loads.

关键词： meta-heuristics self-adaptive algorithms truss sizing optimisation buckling constraints performance comparison

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self-adaptive Micro-Batching for Low-Latency GPU-Accelerated Stream Processing

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2025年第2期53卷 1-24页

作者： Leonarczyk, Ricardo Mencagli, Gabriele Griebler, Dalvan Pontif Catholic Univ Rio Grande Do Sul PUCRS Sch Technol Ave Ipiranga 6681 BR-90619900 Porto Alegre RS Brazil Univ Pisa Comp Sci Dept Largo Bruno Pontecorvo 3 I-56127 Pisa Italy

Stream processing is a computing paradigm enabling the continuous processing of unbounded data streams. Some classes of stream processing applications can greatly benefit from the parallel processing power and affordability offered by GPUs. However, efficient GPU utilization with stream processing applications often requires micro-batching techniques, i.e., the continuous processing of data batches to expose data parallelism opportunities and amortize host-device data transfer overheads. Micro-batching further introduces the challenge of finding suitable micro-batch sizes to maintain low-latency processing under highly dynamic workloads. The research field of self-adaptive software provides different techniques to address such a challenge. Our goal is to assess the performance of six self-adaptive algorithms in meeting latency requirements through micro-batch size adaptation. The algorithms are applied to a GPU-accelerated stream processing benchmark with a highly dynamic workload. Four of the six algorithms have already been evaluated using a smaller workload with the same application. We propose two new algorithms to address the shortcomings detected in the former four. The results demonstrate that a highly dynamic workload is challenging for the evaluated algorithms, as they could not meet the most strict latency requirements for more than 38.5% of the stream data items. Overall, all algorithms performed similarly in meeting the latency requirements. However, one of our proposed algorithms met the requirements for 4% more data items than the best of the previously studied algorithms, demonstrating more effectiveness in highly variable workloads. This effectiveness is particularly evident in segments of the workload with abrupt transitions between low- and high-latency regions, where our proposed algorithms met the requirements for 79% of the data items in those segments, compared to 33% for the best of the earlier algorithms.

关键词： Parallel programming Heterogeneous architectures self-adaptive algorithms GPU programming

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Wavenet fuzzy PID controller for nonlinear MIMO systems: Experimental validation on a high-end haptic robotic interface

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APPLIED SOFT COMPUTING 2016年 40卷 199-205页

作者： Enrique Ramos-Velasco, L. Arturo Dominguez-Ramirez, O. Parra-Vega, V. Univ Autonoma Estado Hidalgo Ctr Invest Tecnol Informac & Sistemas Mineral Reforma Carr Pachuca TulancigoKm 4-5 Hidalgo Mexico IPN CINVESTAV Unidad Saltillo Ctr Invest & Estudios Avanzados Carr Saltillo MonterreyKm 13-5 Ramos Arizpe Coah Mexico

A novel global PID control scheme for nonlinear MIMO systems is proposed and implemented for a robot as study case, this scheme is called AWFPID from its adaptive wavelet fuzzy PID control structure. Basically, it identifies inverse error dynamics using a radial basis neural network with daughter RASP1 wavelets activation function;its output is in cascaded with an infinite impulse response (IIR) filter to prune irrelevant signals and nodes as well as to recover a canonical form. Then, online adaptive fuzzy tuning of a discrete PID regulator is proposed, whose closed-loop guarantees global regulation for nonlinear dynamical plants. The wavelet network includes a fuzzy inference system for online tuning of learning rates. A real-time experimental study on a three degrees of freedom haptic interface, the PHANToM Premium 1.0A, highlights the regulation with smooth control effort without using the mathematical model of the robot. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Identification Wavelet fuzzy neural network self-adaptive algorithms PHANToM robot

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Identification and Wavenet Control of AC Motor

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2013年第3期10卷 269-278页

作者： Ramos Velasco, L. E. Ramos Fernandez, J. C. Islas Gomez, O. Garcia Lamont, J. Espejel Rivera, M. A. Marquez Vera, M. A. Univ Autonoma Estado Hidalgo CITIS ICBI Mineral De La Refonna Hidalgo Mexico Univ Politecn Pachuca Zempoala Hidalgo Mexico Univ Salle Pachuca San Agustin Tlaxiaca Hidalgo Mexico

This paper presents a control scheme to tune online the proportional, integral and derivative gains of a discrete PID controller, through the identification and control of a SISO stable and minimum phase dynamic system. This is accomplished using a radial basis network neural with daughter Morlet wavelets activation functions in cascaded with an infinite impulse response (IIR) filter. This scheme is applied in real time to control the speed of an AC three-phase induction motor supplied with a three-phase inverter. So in this way we show how the identification and control scheme can be implemented in this type of plants that are widely used in industry, without the need of mathematical model parameters of the induction motor. We present numerical simulation and experimental results.

关键词： Motor control PID controller Wavelet neural networks self-adaptive algorithms

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self-adaptive corner detection on MPSoC through resource-aware programming

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JOURNAL OF SYSTEMS ARCHITECTURE 2015年第10期61卷 520-530页

作者： Paul, Johny Oechslein, Benjamin Erhardt, Christoph Schedel, Jens Kroehnert, Manfred Lohmann, Daniel Stechele, Walter Asfour, Tamim Schroeder-Preikschat, Wolfgang Tech Univ Munich Inst Integrated Syst LIS D-80290 Munich Germany Tech Univ Munich D-80290 Munich Germany Univ Erlangen Nurnberg Nurnberg Germany Karlsruhe Inst Technol D-76021 Karlsruhe Germany Karlsruhe Inst Technol High Performance Humanoid Technol Lab D-76021 Karlsruhe Germany Karlsruhe Inst Technol Inst Anthropomat D-76021 Karlsruhe Germany

Multiprocessor system-on-chip (MPSoC) designs offer a lot of computational power assembled in a compact design. In mobile robotic applications, they offer the chance to replace several dedicated computing boards by a single processor, which typically leads to a significant acceleration of the computer-vision algorithms employed. This enables robots to perform more complex tasks at lower power budgets, less cooling overhead and, ultimately, smaller physical dimensions. However, the presence of shared resources and dynamically varying load situations leads to low throughput and quality for corner detection;an algorithm very widely used in computer-vision. The contemporary operating systems from the domain have not been designed for the management of highly parallel but shared computing resources. In this paper, we evaluate resource-aware programming as a means to overcome these issues. Our work is based on Invasive Computing, a MPSoC hardware and operating-system design for resource-aware programming. We evaluate this system with real-world algorithms, like Harris and Shi-Tomasi corner detectors. Our results indicate that resource-aware programming can lead to significant improvements in the behavior of these detectors, with up to 22 percent improvement in throughput and up to 20 percent improvement in accuracy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Corner detection Resource-aware programming Invasive Computing self-adaptive algorithms Computer vision

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Inexact operator splitting methods with selfadaptive strategy for variational inequality problems

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2007年第2期132卷 227-243页

作者： Han, D. Glowinski, R. Nanjing Normal Univ Sch Math & Comp Sci Nanjing Peoples R China

The Peaceman-Rachford and Douglas-Rachford operator splitting methods are advantageous for solving variational inequality problems, since they attack the original problems via solving a sequence of systems of smooth equations, which are much easier to solve than the variational inequalities. However, solving the subproblems exactly may be prohibitively difficult or even impossible. In this paper, we propose an inexact operator splitting method, where the subproblems are solved approximately with some relative error tolerance. Another contribution is that we adjust the scalar parameter automatically at each iteration and the adjustment parameter can be a positive constant, which makes the methods more practical and efficient. We prove the convergence of the method and present some preliminary computational results, showing that the proposed method is promising.

关键词： variational inequality problems operator splitting methods inexact methods self-adaptive algorithms strongly monotone mappings

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self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: Stability analysis and optimization of asymptotic behaviour

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SIGNAL PROCESSING 1999年第3期73卷 225-254页

作者： Charkani, N Deville, Y Labs Elect Philips SAS F-94453 Limeil Brevannes France

In this paper, we investigate the self-adaptive source separation problem for convolutively mixed signals. The proposed approach uses a recurrent structure adapted by a generic rule involving arbitrary separating functions. A stability analysis of this algorithm is first performed. It especially applies to some classical rules for instantaneous and convolutive mixtures that were proposed in the literature but only partly analysed, The expression of the asymptotic error variance is then determined for strictly causal mixtures. This enables to derive the optimum separating functions that minimize this error variance. They are shown to be only related to the probability density functions of the sources. To perform this error minimization, two normalization procedures that improve the algorithm properties are proposed. Their stability conditions and their asymptotic behaviour are analysed. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： asymptotic behaviour optimization blind source separation convolutive mixtures higher-order statistics non-linear algorithms ordinary differential equation recurrent structure self-adaptive algorithms stability analysis

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self-adaptive separation of convolutively mixed signals with a recursive structure. Part II: Theoretical extensions and application to synthetic and real signals

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SIGNAL PROCESSING 1999年第2期75卷 117-140页

作者： Charkani, N Deville, Y Univ Toulouse 3 LAMI F-31400 Toulouse France

This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by generic rules involving arbitrary separating functions. While the basic versions of this approach were defined and analyzed in our companion paper (Charkani and Deville, 1999), two extensions are considered here. The first one is intended for possibly colored signals. In addition, the second one may be used even when the probability density functions of the sources are unknown. We first analyze the convergence properties of these extended approaches at the separating state, i.e. we derive their equilibrium and stability conditions and their asymptotic error variance. We then determine the separating functions which minimize this error variance. We also report experimental results obtained in various conditions, ranging from synthetic data to mixtures of speech signals measured in real situations. These results confirm the validity of the proposed approaches and show that they significantly outperform classical source separation methods in the considered conditions. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： asymptotic behavior optimization blind source separation colored signals convolutive mixtures non-linear algorithms recurrent network self-adaptive algorithms speech processing stability analysis sub-optimum non-linear functions

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Glitch It If You Can: Parameter Search Strategies for Successful Fault Injection 1

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12th International Conference on Smart Card Research and Advanced Application (CARDIS)

作者： Carpi, Rafael Boix Picek, Stjepan Batina, Lejla Menarini, Federico Jakobovic, Domagoj Golub, Marin Riscure BV Delft Netherlands Radboud Univ Nijmegen NL-6525 ED Nijmegen Netherlands Fac Elect Engn & Comp Zagreb Croatia

ISBN: (数字)9783319083025

ISBN: (纸本)9783319083025;9783319083018

Fault analysis poses a serious threat to embedded security devices, especially smart cards. In particular, modeling faults and finding effective practical approaches that are also generic is considered to be of interest for smart card industry. In this work we propose a novel methodology to deal with a difficult question of choosing multiple parameters required for effective faults. To this aim, we investigate several algorithms and find a new promising direction using evolutionary computation. Our experimental results on some of the smart cards used today show the potential of this new approach. Our best algorithm is a tailored search strategy especially developed for the purpose of finding the best choice of parameters for glitching. With this approach we found some of off-the-shelf devices, although secured against this type of attacks, still vulnerable.

关键词： Fault analysis Glitches Smart cards self-adaptive algorithms Evolutionary computation

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self-adaptive Anytime Stream Clustering

Self-Adaptive Anytime Stream Clustering

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9th IEEE International Conference on Data Mining

作者： Kranen, Philipp Assent, Ira Baldauf, Corinna Seidl, Thomas Rhein Westfal TH Aachen Aachen Germany Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781424452422

Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival time;of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.

关键词： stream clustering anytime algorithms self-adaptive algorithms

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