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arXiv 2023年

作者： Jiang, Yuntian He, Chang Zhang, Chuwen Ge, Dongdong Jiang, Bo Ye, Yinyu School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Management Science and Engineering Stanford University United States

The trust-region (TR) method is renowned historically for its robustness in nonconvex problems and extraordinary numerical performance, but the study of its performance in convex optimization is somehow limited. This paper complements the existing literature by presenting a universal trust-region method that simultaneously incorporates the quadratic regularization and ball constraint. In particular, we introduce a novel descent property tailored for trust-region-type algorithms, enabling us to unify and streamline the analysis for both convex and nonconvex optimization. Our method exhibits an iteration complexity of Õ(ϵ−3/2) to find an ϵ-approximate second-order stationary point for nonconvex optimization. Meanwhile, the analysis reveals that the universal method attains an O(ϵ−1/2) complexity bound for convex optimization. Finally, we develop an adaptive universal method to address practical implementations. The numerical results show the effectiveness of our method in both nonconvex and convex problems. © 2023, CC BY.

关键词： optimization algorithms

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Distributed computation of fast consensus weights using ADMM

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AUTOMATICA 2022年 142卷

作者： Rokade, Kiran Kalaimani, Rachel Kalpana Cornell Univ Dept Elect & Comp Engn Ithaca NY 14850 USA Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

In time-critical multi-agent tasks, it is important for the agents to reach consensus as fast as possible. In this paper, we consider the problem of computing the weights in the weighted-average consensus protocol that achieve average consensus with an optimal per-step convergence factor. Most of the work in the literature either computes these optimal set of weights in a centralized manner, which requires global information about the network that may not be available, or computes a suboptimal set of weights, which are slow in achieving consensus. We propose an iterative, distributed algorithm to compute a set of weights that achieve an optimal convergence factor. We give theoretical guarantees of the convergence of the algorithm. Through numerical examples, we show that our method performs better than other distributed methods of computing weights for consensus, and it matches the performance of the centralized optimal method. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Distributed optimization Multi-agent systems Collaborative systems optimization algorithms Large scale optimization problems and methods Lagrangian-based optimization methods

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Channel Estimation for UAV Communication Systems Using Deep Neural Networks

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DRONES 2022年第11期6卷

作者： Al-Gburi, Ahmed Abdullah, Osamah Sarhan, Akram Y. Al-Hraishawi, Hayder Mustansiriyah Univ Dept Comp Sci Baghdad 10052 Iraq Madenat Elem Univ Coll Head Comp Engn Tech Dept Baghdad 10006 Iraq Univ Jeddah Dept Informat Technol Coll Comp & Informat Technol Jeddah 21959 Saudi Arabia Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg Luxembourg

Channel modeling of unmanned aerial vehicles (UAVs) from wireless communications has gained great interest for rapid deployment in wireless communication. The UAV channel has its own distinctive characteristics compared to satellite and cellular networks. Many proposed techniques consider and formulate the channel modeling of UAVs as a classification problem, where the key is to extract the discriminative features of the UAV wireless signal. For this issue, we propose a framework of multiple Gaussian-Bernoulli restricted Boltzmann machines (GBRBM) for dimension reduction and pre-training utilization incorporated with an autoencoder-based deep neural network. The developed system used UAV measurements of a town's already existing commercial cellular network for training and validation. To evaluate the proposed approach, we run ray-tracing simulations in the program Remcom Wireless InSite at a distinct frequency of 28 GHz and used them for training and validation. The results demonstrate that the proposed method is accurate in channel acquisition for various UAV flying scenarios and outperforms the conventional DNNs.

关键词： channel modeling deep learning optimization algorithms unmanned aerial vehicles (UAVs)

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Aquila Optimizer: A novel meta-heuristic optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 157卷 107250-107250页

作者： Abualigah, Laith Yousri, Dalia Abd Elaziz, Mohamed Ewees, Ahmed A. Al-qaness, Mohammed A. A. Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Damietta Univ Dept Comp Dumyat 34517 Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Acad Sci Res & Technol ASRT Cairo Egypt

This paper proposes a novel population-based optimization method, called Aquila Optimizer (AO), which is inspired by the Aquila's behaviors in nature during the process of catching the prey. Hence, the optimization procedures of the proposed AO algorithm are represented in four methods;selecting the search space by high soar with the vertical stoop, exploring within a diverge search space by contour flight with short glide attack, exploiting within a converge search space by low flight with slow descent attack, and swooping by walk and grab prey. To validate the new optimizer's ability to find the optimal solution for different optimization problems, a set of experimental series is conducted. For example, during the first experiment, AO is applied to find the solution of well-known 23 functions. The second and third experimental series aims to evaluate the AO's performance to find solutions for more complex problems such as thirty CEC2017 test functions and ten CEC2019 test functions, respectively. Finally, a set of seven real-world engineering problems are used. From the experimental results of AO that compared with well-known meta-heuristic methods, the superiority of the developed AO algorithm is observed.

关键词： Aquila Optimizer (AO) optimization algorithms Meta-heuristics Real-word problems optimization problems

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Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer

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ENERGIES 2022年第1期15卷

作者： Al-Shamma'a, Abdullrahman A. Omotoso, Hammed O. Alturki, Fahd A. Farh, Hassan. M. H. Alkuhayli, Abdulaziz Alsharabi, Khalil Noman, Abdullah M. King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia

In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.

关键词： photovoltaic parameter extraction Bonobo optimizer renewable energy optimization algorithms

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Optimal Path Planning With Minimum Inspection Teams and Balanced Working Hours For Power Line Inspection

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FRONTIERS IN PHYSICS 2022年 10卷

作者： Hu, Zhao-Long Deng, Yuan-Zhang Peng, Hao Han, Jian-Min Zhu, Xiang-Bin Zhao, Dan-Dan Wang, Hui Zhang, Jun Zhejiang Normal Univ Coll Math & Comp Sci Jinhua Peoples R China

Power line inspection plays a significant role in the normal operation of power systems. Although there is much research on power line inspection, the question of how to balance the working hours of each worker and minimize the total working hours, which is related to social fairness and maximization of social benefits, is still challenging. Experience-based assignment methods tend to lead to extremely uneven working hours among the working/inspection teams. Therefore, it is of great significance to establish a theoretical framework that minimizes the number of working teams and the total working hours as well as balances the working hours of inspection teams. Based on two real power lines in Jinhua city, we first provide the theoretical range of the minimum number of inspection teams and also present a fast method to obtain the optimal solution. Second, we propose a transfer-swap algorithm to balance working hours. Combined with an intelligent optimization algorithm, we put forward a theoretical framework to balance the working hours and minimize the total working hours. The results based on the two real power lines verify the effectiveness of the proposed framework. Compared with the algorithm without swap, the total working hours obtained by the transfer-swap algorithm are shorter. In addition, there is an interesting finding: for our transfer-swap algorithm, the trivial greedy algorithm has almost the same optimization results as the simulated annealing algorithm, but the greedy algorithm has an extremely short running time.

关键词： path planning optimization algorithms complex networks balancing working hours power line inspection

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Quantum algorithms of state estimators in classical control systems

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Science China(Information Sciences) 2020年第9期63卷 243-254页

作者： Kai LI Hongyi DAI Ming ZHANG College of Intelligence Science National University of Defense Technology College of Liberal Arts and Sciences National University of Defense Technology

In this paper, quantum algorithms are applied to the design of state estimators in classical control systems under the condition that quantum algorithms can be physically implemented. We demonstrate that the design of state estimators can be solved by quantum algorithms, which may achieve significant acceleration in comparison to traditional classical algorithms. The time complexity can be reduced from O(n6) to O(qn)when the system matrix is sparse and the condition number κ and the reciprocal of precision ? are small in size O(poly log(n)), where n is the dimension of state x(t) and q is the dimension of input u(t). Our research will provide an entire quantum scheme of constructing state estimators and can be regarded as an attempt to widen application scope of quantum computation.

关键词： quantum computation estimators for linear systems optimization algorithms quantum acceleration time complexity

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FAST EMPIRICAL SCENARIOS

arXiv

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arXiv 2023年

作者： Multerer, Michael Schneider, Paul Sen, Rohan Euler Institute USI Lugano Via la Santa 1 Lugano6962 Switzerland USI Lugano and SFI Via Buffi 6 Lugano6900 Switzerland

We seek to extract a small number of representative scenarios from large panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal selects important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute and lend themselves to consistent scenario-based modeling and multi-dimensional numerical integration that can be used for interpretable decision-making under uncertainty. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed *** Codes 11C20, 41A55, 46E22, 46N30, 60-08, 68W25 Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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CONCURRENT TOPOLOGICAL optimization OF A WHEEL AND BRAKE CALIPER ASSEMBLY

CONCURRENT TOPOLOGICAL OPTIMIZATION OF A WHEEL AND BRAKE CAL...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC-CIE)

作者： Gobbi, Massimiliano Previati, Giorgio Ballo, Federico Politecn Milan Dept Mech Engn I-20158 Milan Italy

ISBN: (纸本)9780791859216

The paper deals with the topological optimization of a wheel and brake caliper assembly. In this system, the design of each components is influenced by the actual shape of the other component. In fact, a conflict exists in the room requirements of the two components. In the design process, therefore, not only the material distribution of the two bodies has to be optimized, but also the design space has to be divided in most effective way. The design of the wheel and brake caliper assembly can be seen as a special class of topological optimization problem. Such problem of concurrent topological optimization of two components sharing part of the design space has been already addressed by the authors in previous papers under the restriction that the two bodies have the same mesh in the shared part of the domain. In this paper, a novel development of the presented optimization algorithm is described. The algorithm is modified in order to allow the concurrent optimization of two bodies with different meshes in the common part of the domain. This new development allows the methodology to be applied to any real problem with arbitrarily complex geometry. The application to the case of the wheel and brake assembly is shown.

关键词： optimization Manufacturing Wheels Brakes Design algorithms Geometry optimization algorithms Shapes

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The SpaceSaving± Family of algorithms for Data Streams with Bounded Deletions

arXiv

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arXiv 2023年

作者： Zhao, Fuheng Agrawal, Divyakant El Abbadi, Amr Mathieu, Claire Metwally, Ahmed de Rougemont, Michel UC Santa Barbara United States CNRS IRIF France Uber Inc. United States University Paris II IRIF France

In this paper, we present an advanced analysis of near optimal algorithms that use limited space to solve the frequency estimation, heavy hitters, frequent items, and top-k approximation in the bounded deletion model. We define the family of SpaceSaving± algorithms and explain why the original SpaceSaving± algorithm only works when insertions and deletions are not interleaved. Next, we propose the new Double SpaceSaving±, Unbiased Double SpaceSaving±, and Integrated SpaceSaving± and prove their correctness. The three proposed algorithms represent different trade-offs, in which Double SpaceSaving± can be extended to provide unbiased estimations while Integrated SpaceSaving± uses less space. Since data streams are often skewed, we present an improved analysis of these algorithms and show that errors do not depend on the hot items. We also demonstrate how to achieve relative error guarantees under mild assumptions. Moreover, we establish that the important mergeability property is satisfied by all three algorithms, which is essential for running the algorithms in distributed settings. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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