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INTEGRATING CODE GENERATION AND PEEPHOLE optimization

ACTA INFORMATICA 1988年第1期25卷 85-109页

作者： GANAPATHI, M FISCHER, CN UNIV WISCONSIN MADISONWI 53706

Peephole optimization when integrated with automatic code generation into a uniform framework has significant advantages in the specification and implementation of efficient compiler back-ends. Attribute grammars provide a framework for expression of machine-specific code optimizations. We present a grammar-driven peephole optimization algorithm that is particularly well suited to attributed-parsing code generators. Integration via semantic attributes corrects interrelated phase-ordering problems and produces a faster and smaller compiler back-end.

关键词： Specifications automatic coding Semantic attribute Code generation code optimization Frameworks syntax optimization algorithms compilers back-end attribute grammars

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Multidisciplinary Design optimization Processes for Efficiency Improvement of Aircraft: State-of-the-Art Review

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2024年 1-23页

作者： Leng, Jun-xue Wang, Zhen-guo Huang, Wei Shen, Yang An, Kai Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China

During actual flight processes, aircraft face various complex operating conditions and must consider the requirements of different disciplines to achieve good overall performance. Multidisciplinary design optimization (MDO) for aircraft is a complex and time-consuming task, making efficiency crucial in aircraft design. This paper starts from the conventional MDO process, covering five aspects including design variables, performance evaluation methods, MDO strategies, optimization algorithms, and knowledge extraction for enhancing MDO efficiency. It introduces multiple techniques and current research status aimed at improving MDO efficiency, and, combined with artificial intelligence, outlines future directions for MDO development. This paper aims to help MDO researchers clarify their thoughts and provide references for advancing current MDO methods.

关键词： Multidisciplinary design optimization Data dimensionality reduction Machine learning MDO strategy optimization algorithms Data mining

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A combined simplex-trust-region method for analog circuit optimization

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2008年第1期17卷 123-140页

作者： Buermen, Arpad Tuma, Tadej Fajfar, Iztok Univ Ljubljana Fac Elect Engn SI-1000 Ljubljana Slovenia

The analog-integrated circuits industry is exerting increasing pressure to shorten the analog circuit design time. This pressure is put primarily on the analog circuit designers that in turn demand automated circuit design tools evermore vigorously. Such tools already exist in the form of circuit optimization software packages but they all suffer a common ailment - slow convergence. Even taking into account the increasing computational power of modern computers the convergence times of such optimization tools can range from a few days to even weeks. Different authors have tried diverse approaches for speeding up the convergence with varying success. In this paper authors propose a combined optimization algorithm that attempts to improve the speed of convergence by exploiting the positive properties of the underlying optimization methods. The proposed algorithm is tested on a number of test cases and the convergence results are discussed.

关键词： computer-aided design analog circuits optimization algorithms

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Research on mechanical optimization methods of cable-driven lower limb rehabilitation robot

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ROBOTICA 2022年第1期40卷 154-169页

作者： Wang, Yan-lin Wang, Ke-yi Chai, Yu-jia Mo, Zong-jun Wang, Kui-cheng Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

In order to improve the working performance of the lower limb rehabilitation robot and the safety of the trained object, the mechanical characteristics of a cable-driven lower limb rehabilitation robot (CDLR) are studied. The dynamic model of the designed CDLR was established. Four kinds of cable tension optimization algorithms were proposed to obtain a good rehabilitation training effect, and the quality of the feasible workspace of the CDLR was analyzed. Finally, a real-time evaluation index of the cable tension optimization algorithms was given to measure the calculation speed of the optimization algorithms. The numerical research results were provided to confirm the characteristics of the four kinds of the optimization algorithms. The research results provide a basis for the follow-up research on the safety and compliance control strategy of the CDLR system.

关键词： Cable-driven robot Rehabilitation robot Mechanical characteristics optimization algorithms Quality of feasible workspace

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Optimal parameters identification for PEMFC using autonomous groups particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 69卷 1113-1128页

作者： Elfar, Medhat Hegazy Fawzi, Mahmoud Serry, Ahmed S. Elsakka, Mohamed Elgamal, Mohamed Refaat, Ahmed Port Said Univ Elect Engn Dept Port Said Egypt Port Said Univ Mech Power Engn Dept Port Said Egypt Mansoura Univ Elect Engn Dept Mansoura Egypt Ural Fed Univ Ural Power Engn Inst Dept Automated Elect Syst Ekaterinburg Russia

The precise model for Polymer Electrolyte Membrane Fuel Cells (PEMFCs) is vital for simulation, control, and performance analysis of PEMFCs. It is crucial to accurately estimate the model parameters. Over the past decade, the extraction of unknown parameters of PEMFCs model is formulated as optimization problem and several metaheuristic techniques have emerged to solve this problem. Despite the development of these techniques to tackle the problem, the slow convergence rate and susceptibility to being trapped in local minima are regarded as weaknesses of these methods. Additionally, because the PEMFC model is a nonlinear and complex model, not all optimization algorithms are suitable for solving it. This article introduces a novel approach that utilizes the Autonomous Groups Particle Swarm optimization (AGPSO) algorithm for extracting precise values for uncertain parameters inherent in PEMFC model of 250Wstack and BCS-500W stack utilized significantly in the literature. The optimization problem's fitness function is formulated as the total squared errors (TSEs) between the voltage datasets measured and estimated. Three different versions of AGPSO algorithm are presented. Statistical analysis is performed on these three versions to assess their robustness, and the most robust version is identified. Furthermore, the performance of the most robust version is extensively tested and analyzed through a complete comparison with recent optimization algorithms findings from the updated state-of-the-art literature such as BO, QOBO, TGA, HHO and ASO. Further, a statistical analysis was carried out that ensured the reliability and robustness of the introduced AGPSO. The results highlight the efficacy and feasibility of AGPSO-based approach across all compared algorithms, demonstrating an enhancement in the accuracy of the PEMFC model. A twophase sensitivity analysis, conducted through Monte Carlo Simulation (MCS), was employed to assess how variations in optimized parameters af

关键词： PEMFC Parameters estimation AGPSO Optimal modelling optimization algorithms

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D-MEANDS-MD: an improved evolutionary algorithm with memory and diversity strategies applied to a discrete, dynamic, and many-objective optimization problem

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KNOWLEDGE ENGINEERING REVIEW 2024年 39卷 e9-e9页

作者： Lafeta, Thiago Fialho de Queiroz Martins, Luiz G. A. Oliveira, Gina M. B. Univ Fed Uberlandia Dept Comp Sci Uberlandia Brazil

Several real-world optimization problems are dynamic and involve a number of objectives. Different researches using evolutionary algorithms focus on these characteristics, but few works investigate problems that are both dynamic and many-objective. Although widely investigated in formulations with multiple objectives, the evolutionary approaches are still challenged by the dynamic multiobjective optimization problems defining a relevant research topic. Some models have been proposed specifically to attack them as the well-known DNSGA-II and MS-MOEA algorithms, which have been extensively investigated on formulations with two or three objectives. Recently, the D-MEANDS algorithm was proposed for dynamic many-objective problems (DMaOPs). In a previous work, D-MEANDS was confronted to DNSGA-II and MS-MOEA solving dynamic many-objective scenarios of the knapsack problem: up to six objectives with five changes or four objectives with ten changes. In this work, we evaluate the behavior of such algorithms in instances up to eight objectives and twenty environmental changes. These enabled us to better understand D-MEANDS weak points which led us to the proposition of D-MEANDSMD. The proposal offers a better balance between memory and diversity. We also included a more recent MOEA in this comparison: the DDIS-MOEA/D-DE. From the results obtained using 27 instances of the dynamic multi- objective knapsack problem, D-MEANDS-MD showed promise for solving discrete DMaOPs compared with the others.

关键词： optimization algorithms

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JamSys: Coverage optimization of a Microphone Jamming System Based on Ultrasounds

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IEEE ACCESS 2019年 7卷 67483-67496页

作者： Shen, Hao Zhang, Weiming Fang, Han Ma, Zehua Yu, Nenghai Chinese Acad Sci Univ Sci & Technol China Key Lab Electromagnet Space Informat Sch Informat Sci & Technol Hefei 230026 Anhui Peoples R China

With the development of digital devices, the recording process has become increasingly easier to conduct. However, the portability of the recording devices has also made recording difficult to monitor. If private conversations are illegally recorded, it will cause serious secret-leakage events. Therefore, it is imperative to prevent unauthorized recordings. Recent works have demonstrated that the nonlinearity effect of microphones can be leveraged to interfere with microphone recording using ultrasounds. However, an ultrasonic array has a limited jamming area. The design of an anti-recording system composed of multiple ultrasonic arrays remains to be addressed. In this paper, a jamming system, JamSys, is presented to prevent eavesdropping in a given region. We propose a new scheme composed of the angle coverage model and the modified harmony search algorithm (MHSA) to optimize the deployment of ultrasonic arrays, which achieves the maximum jamming area with the given number of arrays. In the simulation and experiments, three different optimization algorithms, the MHSA, the genetic algorithm (GA), and the regular coverage algorithm (RCA) are compared. The MHSA is demonstrated to provide the best results.

关键词： Privacy protection anti-recording microphones ultrasounds optimization algorithms

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optimization techniques in respiratory control system models

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APPLIED SOFT COMPUTING 2016年第0期48卷 431-443页

作者： Serna, Leidy Y. Angel Mananas, Miguel Marin, Jesus Mauricio Hernandez, Alher Benito, Salvador Univ Politecn Cataluna CREB Biomed Engn Res Ctr Dept Automat ControlESAII Barcelona Spain CIBER BBN Biomed Res Networking Ctr Bioengn Biomat & Nanome Barcelona Spain Univ Antioquia UdeA Fac Engn Grp GIBIC Bioinstrumentat & Clin Engn Calle 70 52-21 Medellin Colombia Santa Creu & St Pau Hosp Barcelona Barcelona Spain Univ Autonoma Barcelona Dept Med E-08193 Barcelona Spain

One of the most complex physiological systems whose modeling is still an open study is the respiratory control system where different models have been proposed based on the criterion of minimizing the work of breathing (WOB). The aim of this study is twofold: to compare two known models of the respiratory control system which set the breathing pattern based on quantifying the respiratory work;and to assess the influence of using direct-search or evolutionary optimization algorithms on adjustment of model parameters. This study was carried out using experimental data from a group of healthy volunteers under CO2 incremental inhalation, which were used to adjust the model parameters and to evaluate how much the equations of WOB follow a real breathing pattern. This breathing pattern was characterized by the following variables: tidal volume, inspiratory and expiratory time duration and total minute ventilation. Different optimization algorithms were considered to determine the most appropriate model from physiological viewpoint. algorithms were used for a double optimization: firstly, to minimize the WOB and secondly to adjust model parameters. The performance of optimization algorithms was also evaluated in terms of convergence rate, solution accuracy and precision. Results showed strong differences in the performance of optimization algorithms according to constraints and topological features of the function to be optimized. In breathing pattern optimization, the sequential quadratic programming technique (SQP) showed the best performance and convergence speed when respiratory work was low. In addition, SQP allowed to implement multiple non-linear constraints through mathematical expressions in the easiest way. Regarding parameter adjustment of the model to experimental data, the evolutionary strategy with covariance matrix and adaptation (CMA-ES) provided the best quality solutions with fast convergence and the best accuracy and precision in both models. CMAES reach

关键词： Respiratory control system Optimal control optimization algorithms Mechanical work of breathing

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optimization and optimality of (s, S) stochastic inventory systems with non-quasiconvex costs

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2006年第2期20卷 287-306页

作者： Chen, FY Feng, Y Chinese Univ Hong Kong Dept Syst Engn & Engn Management Shatin Hong Kong Peoples R China

This article considers the optimization and optimality of single-item/location, infinite-horizon, (s, S) inventory models. Departing from the conventional approach, we do not assume the loss function describing holding and shortage costs per period to be quasiconvex. As the existing optimization algorithms have been established on the condition of quasiconvexity, our goal in this article is to develop a computational procedure for obtaining optimal (s,S) policies for models with general loss functions. Our algorithm is based on the parametric method commonly used in fractional programming and is intuitive, exact, and efficient. Moreover, this method allows us to extend the optimality of (s, S) policies to a broader class of loss functions that can be non-quasiconvex.

关键词： mathematical programming computational procedure LOSS FUNCTION parametric method infinite horizon optimization algorithms optimality Policies

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A Randomized Block-Coordinate Adam online learning optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12671-12684页

作者： Zhou, Yangfan Zhang, Mingchuan Zhu, Junlong Zheng, Ruijuan Wu, Qingtao Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China

In recent years, stochastic gradient descent (SGD) becomes one of the most important optimization algorithms in many fields, such as deep learning and reinforcement learning. However, the computation of full gradient in SGD is prohibitive when dealing with high-dimensional vectors. For this reason, we propose a randomized block-coordinate Adam (RBC-Adam) online learning optimization algorithm. At each round, RBC-Adam randomly chooses a variable from a subset of parameters to compute the gradient and updates the parameters along the negative gradient direction. Moreover, this paper analyzes the convergence of RBC-Adam and obtains the regret bound,O(T) whereTis a time horizon. The theoretical results are verified by simulated experiments on four public datasets. Moreover, the simulated experiment results show that the computational cost of RBC-Adam is lower than the variants of Adam.

关键词： Adam Convergence Online learning optimization algorithms Randomized block-coordinate Stochastic gradient descent

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