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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Han, Shen Xie, Liangbo Chen, Yanbang Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing University of Posts and Telecommunications International College Chongqing400065 China

ISBN: (纸本)9798350351019

In the domain of indoor wireless positioning technology, non-line-of-sight conditions significantly impact signal ranging accuracy, thereby diminishing the positioning accuracy of conventional least squares and CHAN algorithms. To address this issue, we propose an improved whale optimization algorithm (IWOA). Firstly, the target position equation is established based on the time difference of arrival (TDOA) positioning model. Subsequently, we use IWOA to solve the target position equation. Experimental results demonstrate that IWOA enhances positioning accuracy, affirming the algorithm's effectiveness. © 2024 IEEE.

关键词： optimization algorithms

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A Competitive Swarm Algorithm Based on Tri-Competitive Criterion for Constrained Multiobjective optimization

A Competitive Swarm Algorithm Based on Tri-Competitive Crite...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhou, Jinlong Zhang, Yinggui Yu, Fan Central South University Changsha China

ISBN: (纸本)9798400704956

Constrained multiobjective optimization stands as a focal point in the evolutionary computation community. Some promising algorithms for constrained multiobjective evolutionary optimization have been proposed. However, as the number of decision variables increases, canonical algorithms encounter difficulties. Competitive swarm optimizer (CSO) has demonstrated highly promising performance. In classical CSO, only half of the particles are updated in each iteration. This may lead to an inefficient search of the optimizer under limited computational resources. To address the issue, this study proposes a competitive swarm algorithm based on a tri-competitive criterion, aiming to enhance the search efficiency of the algorithm in high-dimensional decision variable scenarios. Specifically, two-thirds of the particle swarm is updated in each iteration by introducing the tri-competitive criterion. Moreover, to deal with various constraints, we use an μ method that integrates principles from multiobjective optimization. The computational experiments were conducted on two benchmark test suites with up to 100 decision variables. The experimental results indicate that the proposed algorithm has demonstrated highly competitive performance compared to peer algorithms. © 2024 held by the owner/author(s).

关键词： optimization algorithms

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STATISTICAL optimization WITH AVERAGING USING A MODE OF SIX 38

STATISTICAL OPTIMIZATION WITH AVERAGING USING A MODE OF SIX

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38th Annual European Simulation and Modelling Conference, ESM 2024

作者： Conley, William Austin Cofrin School of Business University of Wisconsin at Green Bay 443 Wood Hall Green BayWI54311 United States

ISBN: (纸本)9789492859334

Multi Stage Monte Carlo optimization (MSMCO), also called statistical optimization, is generally used on a complex nonlinear multivariate problem that classical mathematics has difficulty solving. The Fundamental Theorem of nonlinear optimization could be stated that the optimal solution can be anywhere in the feasible solution space. © 2024 Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024. All rights reserved.

关键词： optimization algorithms

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Urban Emergency Response optimization Algorithm Based on Deep Learning 5

Urban Emergency Response Optimization Algorithm Based on Dee...

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5th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Luo, Ling Qi, Xiaoman Wang, Chen State Grid Shanghai Electric Power Research Institute Shanghai200437 China

ISBN: (纸本)9798331527662

Effective and rapid emergence approaches and responses remain critical in minimizing the impacts of accidents and disasters in the modern urban environment. This article, therefore, draws attention to optimization algorithms that are effectively used in urban emergency response. These efforts will play a fundamental role in leveraging their capabilities through deep learning. These algorithms are primarily designed to analyze a significant amount of urban emergency data from historical incident data, traffic patterns, and population densities—to predict and optimize emergency response approaches. This article examines the integration of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in the temporal and spatial data analysis process. The experimental results reveal improvements in resource allocation and response times in rapid emergencies compared to the traditional models. The examined model not only plays a critical role in enhancing the accuracy and speed of emergency response but also adapts to the significantly changing conditions within urban settings, providing an effective and robust solution to emergency management in emerging smart cities. © 2024 IEEE.

关键词： Automated Systems Deep Learning Systems Disaster Management Systems optimization algorithms Urban Emergency Response

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Research on the clustering competition coevolution optimization framework under the parallel lion swarm optimization algorithm 24

Research on the clustering competition coevolution optimizat...

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2024 9th International Conference on Mathematics and Artificial Intelligence, ICMAI 2024

作者： Jiang, Keqin Jiang, Mingyan Han, Zongxin Wang, Feng Zhao, Ze School of information science and engineering Shandong university Qingdao266237 China

ISBN: (纸本)9798400717284

This study introduces a framework for clustering competition coevolution optimization algorithm based on the parallel Lion Swarm optimization Algorithm (LSO). This framework combines clustering and competitive coevolution concepts under existing parallel computing paradigms. Initially, clustering categorizes particles of the total population, followed by parallel computing principles where particles within each classified subpopulation undergo local optimization using distinct optimization mechanisms. After a certain number of iterations, these subpopulations coevolve through an island-based topology. Experimental results demonstrate significant advantages of the proposed algorithm over traditional methods in both CEC2013 benchmark functions and feature selection problems, affirming its potential and effectiveness in practical applications. This framework introduces a novel approach and method for addressing complex problems, offering broad prospects for application. 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： optimization algorithms

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Fairness aware secure energy efficiency maximization for UAV-assisted data collection in backscattering networks

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VEHICULAR COMMUNICATIONS 2025年 52卷

作者： Zeng, Jiawang Mishra, Deepak Gharakheili, Hassan Habibi Seneviratne, Aruna Univ New South Wales Sch Elect Engn & Telecommun Sydney Australia

Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multiUAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.

关键词： Vehicle control optimization algorithms Rotary-wing UAV communications Backscatter networks Security protocol Energy efficiency

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Parameters Estimation of Photovoltaic Solar Cell Using a Stochastic optimization Approach 7th

Parameters Estimation of Photovoltaic Solar Cell Using a Sto...

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7th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2023

作者： Djeblahi, Zahia Mahdad, Belkacem Srairi, Kamel Hammoudi, Mohamed Yacine Veysel Murat İstemihan, Genç Science and Technology College Biskra University Biskra Algeria Electrical Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031606311

The use of solar photovoltaics to produce electricity is becoming more widespread. The search for the best optimal solar PV models presents the most challenge, owing to their non-linear current versus voltage characteristics. Despite the application of various optimization techniques to estimate the parameters of PV systems, there remains an opportunity to attain the most optimized outcomes. In this paper, a new hybrid stochastic optimization approach called Fitness-Distance-Balance based Adaptive Guided Differential Evolution (FDB-AGDE) algorithm combined with Newton Rapson (NR) method to estimate solar PV parameters panels. The inappropriate fitness function (the root means square error (RMSE)) was utilized in previous research to extract the PV parameters of solar models. Experimental results show that the proposed approach performs better compared to other algorithms. In the case of the single and double-diode models, a comparison between the computed data and the observed data for FDB-AGDE reveals the superiority of this method in estimating the PV parameters of the single and double-diode Models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： optimization algorithms

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Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions

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SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Jamaludin, Mohd Nasrul Izzani Tajuddin, Mohammad Faridun Naim Younis, Tarek Thanikanti, Sudhakar Babu Khishe, Mohammad Univ Malaysia Perlis Fac Elect Engn & Technol Arau 02600 Malaysia Aswan Univ Dept Elect Engn Aswan 81528 Egypt Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Jadara Univ Res Ctr Irbid Jordan

The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. As long as the rate of change of irradiance does not exceed a specific limit, the HC mode is applied to track the global maximum power point (GMPP). Once a high rate of change in irradiation is detected, the SSA mode is activated. Moreover, the proposed algorithm employs the concept of boundary conditions to handle fast and slow fluctuating irradiance patterns. A comprehensive comparative evaluation of the proposed hybrid SSA-HC with state-of-the-art MPPT algorithms has been undertaken. Four distinct cases have been examined, including irradiance conditions with varying rates of change and partial shading conditions. The proposed hybrid SSA-HC algorithm has been validated and tested using a developed hardware setup, simulated in MATLAB for solar photovoltaic (PV) systems, and compared with standard SSA and HC. The performance of the tracking capability of this proposed hybrid technique at both steady-state and dynamic conditions under rapid and gradual irradiance changes demonstrates its superiority over recent state-of-the-art algorithms.

关键词： Maximum power point tracking optimization algorithms Fluctuating environments Partial shading Solar PV system

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optimization Algorithm Synthesis based on Integral Quadratic Constraints: A Tutorial 62

Optimization Algorithm Synthesis based on Integral Quadratic...

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62nd IEEE Conference on Decision and Control (CDC)

作者： Scherer, Carsten W. Ebenbauer, Christian Holicki, Tobias Univ Stuttgart Dept Math Stuttgart Germany Rhein Westfal TH Aachen Chair Intelligent Control Syst Aachen Germany

ISBN: (纸本)9798350301243

We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design accelerated gradient descent algorithms with optimal guaranteed convergence rates by solving small-sized convex semi-definite programs. It is shown that this extends to the design of extremum controllers, with the goal to regulate the output of a general linear closed-loop system to the minimum of an objective function. Numerical experiments illustrate that we can not only recover gradient decent and the triple momentum variant of Nesterov's accelerated first order algorithm, but also automatically synthesize optimal algorithms even if the gradient information is passed through non-trivial dynamics, such as time-delays.

关键词： optimization algorithms Robust Control Linear Matrix Inequalities

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Efficient Hyper-parameter optimization with Cubic Regularization 37

Efficient Hyper-parameter Optimization with Cubic Regulariza...

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37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Shen, Zhenqian Yang, Hansi Li, Yong Kwok, James Yao, Quanming Tsinghua Univ Dept Elect Engn Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781713899921

As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning. In this paper, we consider a sub-class of hyper-parameter optimization problems, where the hyper-gradients are not available. Such problems frequently appear when the performance metric is non-differentiable or the hyper-parameter is not continuous. However, existing algorithms, like Bayesian optimization and reinforcement learning, often get trapped in local optimals with poor performance. To address the above limitations, we propose to use cubic regularization to accelerate convergence and avoid saddle points. First, we adopt stochastic relaxation, which allows obtaining gradient and Hessian information without hyper-gradients. Then, we exploit the rich curvature information by cubic regularization. Theoretically, we prove that the proposed method can converge to approximate second-order stationary points, and the convergence is also guaranteed when the lower-level problem is inexactly solved. Experiments on synthetic and real-world data demonstrate the effectiveness of our proposed method.

关键词： optimization algorithms

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