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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Evich, L. N. Ostroukh, E. N. Panasenko, P. A. Don State Tech Univ Dept Informat Technol Dept Media Commun & Multimedia Technol Rostov Na Donu Russia Krasnodar Higher Mil Sch Dept Sci Work Krasnodar Russia

ISBN: (纸本)9781538643075

Popular algorithms of global optimization are reviewed. Comparison of two hybrid algorithms is presented. In particular, consecutive and conveyor hybridization of Artificial bee colony (ABC) and Gravitational Search Algorithm (GSA) is introduced. The analysis of two algorithms work productivity based on spherical function, Rosenbrak, Girvonk, Rastrigin, Shevfel functions is performed. Efficiency of algorithm with conveyor hybridization compared to algorithm with consecutive hybridization is set for determined parameters.

关键词： optimization algorithms hybrid algorithms genetic algorithms bee colony gravitational algorithm

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Accelerated algorithms for Constrained Nonconvex-Nonconcave Min-Max optimization and Comonotone Inclusion 41

Accelerated Algorithms for Constrained Nonconvex-Nonconcave ...

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41st International Conference on Machine Learning (ICML)

作者： Cai, Yang Oikonomou, Argyris Zheng, Weiqiang Yale Univ Dept Comp Sci New Haven CT 06520 USA

We study constrained comonotone min-max optimization, a structured class of nonconvexnonconcave min-max optimization problems, and their generalization to comonotone inclusion. In our first contribution, we extend the Extra Anchored Gradient (EAG) algorithm, originally proposed by Yoon & Ryu (2021) for unconstrained min-max optimization, to constrained comonotone min-max optimization and comonotone inclusion, achieving an optimal convergence rate of O (1/T) among all first-order methods. Additionally, we prove that the algorithm's iterations converge to a point in the solution set. In our second contribution, we extend the Fast Extra Gradient (FEG) algorithm, as developed by Lee & Kim (2021), to constrained comonotone min-max optimization and comonotone inclusion, achieving the same O (1/T) convergence rate. This rate is applicable to the broadest set of comonotone inclusion problems yet studied in the literature. Our analyses are based on simple potential function arguments, which might be useful for analyzing other accelerated algorithms.

关键词： optimization algorithms

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optimization of the Stop-and-Go- and Dual-Mode-algorithms for Real-time Baseband Transmission 18

Optimization of the Stop-and-Go- and Dual-Mode-Algorithms fo...

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18th IEEE Conference on Signal Processing - algorithms, Architectures, Arrangements, and Applications (SPA)

作者： Meyer, Stephan Fraunhofer Inst Commun Informat Proc & Ergon Software Defined Radio Commun Syst Wachtberg Germany

ISBN: (纸本)9788362065189

This paper presents two optimized blind equalization algorithms, developed on a real-time digital signal processor (DSP) test bed. The most commonly used group of blind channel equalizers are those based on the Bussgang algorithm. By combining a joint algorithm (JA) with a dual-mode (DM) algorithm or a stop-and-go (SAG) algorithm exists the option for optimal matching to the channel behavior. The performance of a single algorithm like modified decision directed modulus (MDDMA), modified constant modulus (MCMA) or the modified decision directed (MDDA) algorithm can only be adapted by adjusting the step size for updating the equalizer tap vector or by changing the number of equalizer taps. Using SAG or DM algorithm introduces a flag (SAG) or a confidence zone (DM) to optimize the algorithm performance further. Combining the joint algorithm within the SAG or DM algorithm yields an optimized algorithm which offers the advantage of the different algorithms. The new algorithms lead to a decrease of the required transmit power by about 22 percent while achieving the same BER. Additional to this, a 6 percent reduction of the processing time is possible relative to MDDMA due to the possibility to reduce the equalizer length.

关键词： SAG SAG gene Equalizers step size algorithms Transmitter power Baseband transmission HELIOS DEVICES optimization algorithms Percent Optimal matching processing time

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SUPPRESSION OF HIGH FREQUENCY OSCILLATIONS ON SUSPENSION SYSTEM OF SNOW GROOMER WITH EVOLUTIONARY algorithms optimization 9

SUPPRESSION OF HIGH FREQUENCY OSCILLATIONS ON SUSPENSION SYS...

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9th FPNI Ph D Symposium on Fluid Power (FPNI2016)

作者： Marani, Pietro Ferrari, Cristian IMAMOTER CNR Ferrara Italy

ISBN: (纸本)9780791850473

In current engineering practice for the design and dimensioning of hydropneumatic suspension systems, the effect of main parameters is considered;this approach can be used to implement approximate models basically suitable to describe low frequency and high amplitude oscillations of the machine. The target of this study is a Snow Groomer, a tracked vehicle driven by diesel engines and equipped in front with a shovel and behind with a cutter. When the machine drives over a snowfield, it pushes snow ahead of it and, at the same time, smooths out any surface unevenness. The suspension system is the key element to ensure the driver's safety and comfort, the effectiveness of snow grooming and finally enhance the reliability of the machine components. The on-field testing had shown high frequency pressure oscillations transmitted from the sprocket to hydraulic system, propagated through the flexible hoses. Those Pressure Oscillations cause noise and can affect negatively the durability and reliability of the Machine. A lumped parameter non-linear dynamic model of the hydraulic circuit and of the machine interactions is built in Amesim environment, including Lax Wendroff wave propagation models, to make it able to catch the high frequency oscillations experienced in the test field. Most of the design parameters are fixed (such as vehicle weight and hydraulic lines length), other parameters can be varied to study the optimal solution, these parameters define the "factors" of the optimization problem. As a next step it is important to define the objectives of the optimization, in this case corresponding to various figures of merit describing the behavior of the system in different work conditions. The large number of factors included in the lumped parameter model generates an exponentially larger number of possible configurations. Moreover the relationship between factors and objective is not always possible to express with explicit mathematical models. Finally the presence

关键词： optimization Suspension systems Pressure Tracked vehicles Design Genetic algorithms Machine components Evolutionary algorithms Lumped parameter models Testing Hydraulic circuits algorithms Oscillations optimization algorithms Durability Dimensions Diesel engines Safety Noise (Sound) Vehicles Reliability Simulation Pareto optimization Particulate matter Dynamic models Machinery Wave propagation Weight (Mass) Hydraulic drive systems

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Enhanced Electrical Characterization of Dusty Solar Modules: Integrating Outdoor Experiments With Single- and Double-Diode Models

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2025年第1期2025卷

作者： Younis, Abubaker Belabbes, Fatima Cotfas, Petru Adrian Cotfas, Daniel Tudor Transilvania Univ Brasov Elect & Comp Dept Brasov Romania Univ Djillali Liabes Sidi Bel Abbes Dept Elect Sidi Bel Abbes Algeria

This study addresses a research gap regarding the impact of dust accumulation on photovoltaic (PV) modules, with a specific focus on parameter extraction using single- and double-diode models (SDMs and DDMs) under dusty conditions. While dust effects on PV performance are well-studied, few have explored how existing models can accurately represent these effects. Experimental data from outdoor testing of small-scale modules subjected to artificially deposited dust were analyzed. The direct current parameters were then extracted using the SDM and DDM, with the application of the improved snake optimization algorithm to enhance the accuracy. Preliminary analysis shows that the fill factor of dusty panels gradually increases, surpassing that of clean panels, due to increased absorption of diffuse light from reflections off the nonuniform dust layer. Efficiency uniformly decreases under dust presence. Computational comparison reveals a significant impact of dust on the algorithm's prediction quality, with maximum root mean square error decreases of 339.1% and 303.5% for DDM and SDM, respectively. The study observes that DDM effectively represents dust effects with fewer parameters than SDM, which includes more parameters conveying dust deposition effects. On average, DDM photocurrent values decrease by 24.2% due to dust, while shunt resistance decreases by 79.7%. For SDM, photocurrent decreases by 24.2%, shunt resistance by 80.1%, diode saturation current by 84.6%, and ideality factor by 10.5%. These findings suggest that current models inadequately represent dust effects, favoring SDM for its simplicity, while partial shading serves as a weak approximation.

关键词： dust soiling equivalent circuit model optimization algorithms parameter identification photovoltaics sustainable development goal 7

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Efficient algorithms for Empirical Group Distributionally Robust optimization and Beyond 41

Efficient Algorithms for Empirical Group Distributionally Ro...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Dingzhi Cai, Yunuo Jiang, Wei Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University Shanghai China Guangzhou China

In this paper, we investigate the empirical counterpart of Group Distributionally Robust optimization (GDRO), which aims to minimize the maximal empirical risk across m distinct groups. We formulate empirical GDRO as a two-level finite-sum convex-concave minimax optimization problem and develop an algorithm called ALEG to benefit from its special structure. ALEG is a double-looped stochastic primal-dual algorithm that incorporates variance reduction techniques into a modified mirror prox routine. To exploit the two-level finite-sum structure, we propose a simple group sampling strategy to construct the stochastic gradient with a smaller Lipschitz constant and then perform variance reduction for all groups. Theoretical analysis shows that ALEG achieves Ε-accuracy within a computation complexity of (equation presented), where n¯ is the average number of samples among m groups. Notably, our approach outperforms the state-of-the-art method by a factor of √m. Based on ALEG, we further develop a two-stage optimization algorithm called ALEM to deal with the empirical Minimax Excess Risk optimization (MERO) problem. The computation complexity of ALEM nearly matches that of ALEG, surpassing the rates of existing methods. Copyright 2024 by the author(s)

关键词： optimization algorithms

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A seasonal grey model for forecasting energy imports demand from information differences perspective

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APPLIED MATHEMATICAL MODELLING 2025年 140卷

作者： Zhou, Weijie Chang, Jiaxin Zuo, Weizhen Wang, Feifei Changzhou Univ Sch Wujinglian Econ Changzhou 213159 Peoples R China Changzhou Univ Business Coll Changzhou 213159 Peoples R China

This paper focuses on the critical task of accurately predicting seasonal energy import demand for countries heavily reliant on foreign energy supplies. Given the significance of optimizing energy supply structures and formulating effective policies, precise forecasting is imperative. However, due to complex cyclical fluctuations, it is challenging to construct a prediction model that can handle seasonal sequences with multiple patterns and trends. And the construction of the model relies on the extraction of data information. Based on the differences impacts between new and old data information on future predictions, this paper proposes a new seasonal grey power model using new-information-priority principle. Nine types of seasonal series with different cycles, trends and patterns, and lengths are used to test the adaptability of the new model. Among these, the seasonal data distributions, and modelling effect of daily and hourly series are studied for the first time in grey forecasting system. Furthermore, three monthly energy imports demand, including natural gas, liquefied petroleum gas, crude oil imports are utilized to verify fitting and predicting accuracy of the proposed model. The results demonstrate that the new model exhibits higher prediction accuracy and lower volatility compared to other models, containing prevailing grey models, machine learning models, and statistical models. Besides, the performance of different swarm intelligent algorithms, such as Cultural Algorithm, Genetic Algorithm, Particle Swarm optimization, and Simulated Annealing optimization algorithms in hyper-parameter optimization of the new model is analyzed. Finally, the new model generates forecasts for three energy imports demands each month from 2023 to 2026. And some practical implications and policy suggestions are also discussed.

关键词： Data trends and patterns Energy imports demand New-information-priority Seasonal grey power model Application scope analysis optimization algorithms

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Stochastic population update can provably be helpful in multi-objective evolutionary algorithms

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ARTIFICIAL INTELLIGENCE 2025年 341卷

作者： Bian, Chao Zhou, Yawen Li, Miqing Qian, Chao Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing 210023 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the best solutions from the current population and newly-generated solutions (irrespective of the selection criteria used such as Pareto dominance, crowdedness and indicators). In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.

关键词： optimization algorithms

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Contextually Adaptive algorithms for Gaussian Process Bandit optimization Under Heavy-Tailed Noise 27

Contextually Adaptive Algorithms for Gaussian Process Bandit...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Park, Hyeonjun Lee, Kyungjae The Department of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of The Department of Statistics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9781643685489

We consider a Gaussian process (GP) bandit optimization problem when the objective function lives in a reproducing kernel Hilbert space (RKHS), assuming that the payoffs follow a heavy-tailed distribution with a bounded (1 + e)-th moment for some e ∈ (0, 1].Existing algorithms for this setting face practical challenges due to their significant computational demands and inconsistent theoretical guarantee to translation of noise *** address these issues, we introduce two robust *** first algorithm utilizes a truncation estimator, achieving the same regret bound as that of the existing algorithm up to logarithmic terms with reduced time *** second algorithm employs a median-of-means estimator and achieves more stable regret bound to alteration of noise distribution with lower time and space complexities compared to existing ***, we empirically validate the performance of our proposed algorithms against previous methods in both synthetic and real-world datasets. © 2024 The Authors.

关键词： optimization algorithms

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Barrier algorithms for Constrained Non-Convex optimization 41

Barrier Algorithms for Constrained Non-Convex Optimization

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41st International Conference on Machine Learning, ICML 2024

作者： Dvurechensky, Pavel Staudigl, Mathias Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany University of Mannheim Mannheim Germany

In this paper, we theoretically show that interior-point methods based on self-concordant barriers possess favorable global complexity beyond their standard application area of convex optimization. To do that we propose first- and second-order methods for non-convex optimization problems with general convex set constraints and linear constraints. Our methods attain a suitably defined class of approximate first- or second-order KKT points with the worst-case iteration complexity similar to unconstrained problems, namely O(Ε-2) (first-order) and O(Ε-3/2) (second-order), respectively. Copyright 2024 by the author(s)

关键词： optimization algorithms

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