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Benchmarking Optimization Methods Enabling Efficient Designs for Diverse Nanophotonic Applications

ADVANCED OPTICAL MATERIALS 2025年

作者： Lee, Chihun Rho, Junsuk Pohang Univ Sci & Engn POSTECH Dept Mech Engn Pohang 37673 South Korea Korea Inst Mat Sci KIMS Mat Data & Anal Res Div Chang Won 51508 South Korea Pohang Univ Sci & Engn POSTECH Dept Chem Engn Pohang 37673 South Korea Pohang Univ Sci & Engn POSTECH Dept Elect Engn Pohang 37673 South Korea POSCO POSTECH RIST Convergence Res Ctr Flat Opt & Pohang 37673 South Korea

Advances in fabrication processes and materials have driven the demand for intricate nanophotonic designs that enable active, multifunctional, and high-performance devices. Selecting an appropriate optimization method is crucial, considering the characteristics of both the problem and algorithms, such as the number of design parameters and simulation methods. In this study, nine optimization algorithms are modularized and applied them to six representative nanophotonic problems using rigorous coupled-wave analysis (RCWA) and scalar diffraction theory. This framework facilitates benchmarking and performance evaluation of optimization methods across diverse problems. Particle swarm optimization (PSO) excels in low-dimensional cases, while genetic algorithms (GAs) performs better in higher dimensions. For optimizing phase profiles with tens of thousands of variables, automatic differentiation-based methods are most effective. This research provides a practical guide for selecting optimization methods based on problem characteristics and lays the foundation for modular, scalable inverse design tools in nanophotonics.

关键词： artificial intelligence benchmark computational nanophotonics global optimization intelligent algorithm inverse design nanophotonics numerical optimization optimization algorithms photonic devices

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intelligent analysis system for signal processing tasks based on LSTM recurrent neural network algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第15期34卷 12257-12269页

作者： Zhou, Ya Jiao, Xiaobo Xuchang Univ Sch Elect & Mech Engn Xuchang 461000 Peoples R China State Grid Xuchang Power Supply Co Internet Dept Xuchang 461000 Peoples R China

In order to improve the effectiveness of signal processing, according to the actual needs of signal processing and the current problems in signal processing, this paper introduces an improved LSTM recurrent neural network algorithm to construct the signal processing algorithm. Moreover, this paper sets up the functional structure of this paper based on the neural network model structure, and builds an intelligent analysis system for signal processing tasks based on the LSTM recurrent neural network algorithm. In addition, this paper analyzes the system algorithm flow in detail, and combines experimental research to carry out the effectiveness of the system constructed in this paper, and conducts quantitative analysis from two aspects of signal threshold prediction and signal processing effect. Finally, this paper conducts experimental results research with the support of mathematical statistics methods. From the research point of view, it can be known that the system constructed in this paper has good signal processing functions.

关键词： LSTM recurrent neural network intelligent algorithm Signal processing intelligent analysis

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Autonomous Task Planning of intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

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DRONES 2025年第4期9卷

作者： Jiang, Qiang Yan, Yongzhao Dai, Yinxing Yang, Zequan Cao, Huazhen Wang, Bo Ma, Xiaoping Chinese Acad Sci Inst Engn Thermophys Natl Key Lab Sci & Technol Adv Light Duty Gas Turb Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Cent South Univ Sch Automat Changsha 410075 Peoples R China Qingdao Inst Aeronaut Technol Qingdao 266400 Peoples R China

intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence.

关键词： swarm autonomous decision-making simulation environment intelligent algorithm

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Research and Simulation Analysis of Fuzzy intelligent Control System algorithm for a Servo Precision Press

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APPLIED SCIENCES-BASEL 2024年第15期14卷

作者： He, Yanzhong Luo, Xiang Wang, Xingsong Southeast Univ Sch Mech Engn Nanjing 211102 Peoples R China

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

关键词： press high-precision servo controller intelligent algorithm fuzzy control stability block diagram Simulink simulation

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Electrical capacitance tomography and parameter prediction based on particle swarm optimization and intelligent algorithms

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WIRELESS NETWORKS 2021年 1-12页

作者： Zhang, Yanpeng Chen, Deyun Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

Electrical capacitance tomography is an industrial process tomography technology, mainly used to measure and plot two-phase flow and multi-phase flow. The technology is based on the properties of various dielectric constants between the phases of the measured substance. This work focuses on the improvement of particle swarm optimization and intelligent algorithms, especially for the parameter control of particle swarm optimization and intelligent algorithms. The efficiency of group optimization algorithms and intelligent algorithms that solve optimization problems. This paper mainly aims at the image reconstruction process in electrical capacitance tomography system, and proposes an image reconstruction algorithm based on intelligent algorithm and particle swarm algorithm. Combined with the experimental environment of the self-made electrical capacitance tomography system, the actual imaging effect of the algorithm was compared and analyzed with the traditional imaging algorithm, and the verification of the algorithm improvement effect was theoretically completed. According to the finite element analysis method, the internal area of the sensor is subdivided on the entire network, and three flow modes are modeled, which provides conditions for the construction of the following experimental environment. Based on the theory, the principle of the classic Landweber imaging algorithm is discussed in detail. Using the experimental environment with built-in electrical capacitance tomography system, experiments were conducted to visualize the gas-liquid two-phase flow. The traditional Landweber algorithm and imaging algorithm proposed in this paper are used to reconstruct the image using the obtained volume data. Through comparative analysis of the resulting images, the results show that the imaging algorithm proposed in this paper improves the accuracy of flow pattern recognition and image accuracy, which proves the improvement. The feasibility of algorithm and particle swa

关键词： Particle swarm optimization intelligent algorithm Electrical capacitance tomography Parameter prediction

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Research on tasks schedule and data transmission of video sensor networks based on intelligent agents and intelligent algorithms

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The Journal of China Universities of Posts and Telecommunications 2009年第6期16卷 84-91,112页

作者： HUANG Hai-ping WANG Ru-chuan SUN Li-juan WANG Hai-yuan XIAO Fu College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance *** on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data *** article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with *** simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization.

关键词： video sensor networks intelligent algorithm tasks schedule optimal routing agent

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A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing intelligent algorithms Based on BWM and Group VIKOR Techniques

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2020年第3期19卷 909-957页

作者： Abdulkareem, Karrar Hameed Arbaiy, Nureize Zaidan, A. A. Zaidan, B. B. Albahri, O. S. Alsalem, M. A. Salih, Mahmood M. Univ Pendidikan Sultan Idris Dept Comp Tanjong Malim Perak Malaysia Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Al Muthanna Univ Coll Agr Samawah 66001 Iraq Univ Mosul Coll Adm & Econ Mosul Iraq Tikrit Univ Dept Comp Sci Comp Sci & Math Coll Tikrit 34001 Iraq

The increasing demand for image dehazing-based applications has raised the value of efficient evaluation and benchmarking for image dehazing algorithms. Several perspectives, such as inhomogeneous foggy, homogenous foggy, and dark foggy scenes, have been considered in multicriteria evaluation. The benchmarking for the selection of the best image dehazing intelligent algorithm based on multi-criteria perspectives is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, (c) data variation, (d) criteria conflict, and (e) criteria tradeoff. A generally accepted framework for benchmarking image dehazing performance is unavailable in the existing literature. This study proposes a novel multi-perspective (i.e., an inhomogeneous foggy scene, a homogenous foggy scene, and a dark foggy scene) benchmarking framework for the selection of the best image dehazing intelligent algorithm based on multi-criteria analysis. Experiments were conducted in three stages. First was an evaluation experiment with five algorithms as part of matrix data. Second was a crossover between image dehazing intelligent algorithms and a set of target evaluation criteria to obtain matrix data. Third was the ranking of the image dehazing intelligent algorithms through integrated best-worst and VIseKriterijumska Optimizacija I Kompromisno Resenje methods. Individual and group decision-making contexts were applied to demonstrate the efficiency of the proposed framework. The mean was used to objectively validate the ranks given by group decision-making contexts. Checklist and benchmarking scenarios were provided to compare the proposed framework with an existing benchmark study. The proposed framework achieved a significant result in terms of selecting the best image dehazing algorithm.

关键词： Multi-criteria decision making intelligent algorithm image dehazing benchmarking BWM VIKOR

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State of charge estimation for Li-ion battery based intelligent algorithms

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ELECTRICAL ENGINEERING 2023年第2期105卷 1179-1197页

作者： Degla, Aicha Chikh, Madjid Mzir, Mahdi Belabed, Youcef Ctr Dev Energies Renouvelables CDER Algiers 16340 Algeria Natl Polytech Sch Hassen Badi Lab Commun Equipment & Photovolta Energy Convers El Harrache Algeria Ctr Dev Energies Renouvelables Unite Dev Equipements Solaires Tipasa 42004 Algeria CEREFE Algiers 16000 Algeria

State of charge (SOC) is a crucial index for a battery's energy assessment. Its estimation is becoming an increasing challenge in order to assure the battery's safety and efficiency. To this end, many methods can be found in the scientific literature with various accuracy and complexity. However, accurate SOC is highly dependent on the adopted methodology. This paper investigates five methods for estimating battery SOC for lithium-ion (Li-ion) manufacturers. For this purpose, five methods were selected and then used in practice, including the modified Coulomb counting method, the extended Kalman filter, the neural network (NN), and two other techniques based on machine learning known as the support vector machines and the K-nearest neighbor algorithm (KNN), respectively. A detailed analysis based on statistical assessment is performed on an experimental test that covers multiple cycles of charge and discharge modes. The KNN method proved to be more accurate than the EKF approach, which is extensively used for estimating the SOC of Li-ion batteries. The algorithm demonstrated great predictive accuracy, with most predictions having a relative error near zero and a maximum error value of roughly 0.26%. All of the findings validate the methodology's reliability and efficiency.

关键词： Li-ion battery SOC estimation PV system intelligent algorithm Battery storage

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Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Huang, Ju Nan, Jingchang Gao, Mingming Wang, Yifei Liaoning Univ Engn & Technol Sch Elect & Informat Engn Shenyang Liaoning Peoples R China Liaoning Univ Engn & Technol Sch Elect & Informat Engn Fuxin Henan Peoples R China Liaoning Univ Engn & Technol Sch Elect & Informat Engineerin Fuxin Inner Mongolia Peoples R China Liaoning Univ Engn Technol Sch Elect & Informat Engineerin Fuxin Peoples R China

As wireless communication technology continues to advance, the antenna, as an essential front -end device in radio communication system, is surrounded by more and more complex electromagnetic wave environments with increasing variety, resulting in greater demand for antennas and higher design requirements. While the traditional antenna design methods suffered from the disadvantage of low design efficiency, a powerful tool for accelerating antenna design is the modelling of antennas with neural networks. Aiming to enhance the modeling accuracy of neural network, multiple novel meta-heuristic swarm intelligent algorithms are introduced and part of them are modified for the purpose of applying to optimizing network ' s weights and biases so as to raise the antenna model`s prediction precision on the basis of neural network. Specifically, the intelligent algorithms and their improvement directions include the strategy of optimizing weights and biases for neural networks with seagull optimization algorithm, optimizing the weights and biases of neural network with the improved butterfly algorithm fused with reverse learning, and the artificial rabbit algorithm optimizing the neural network weights and biases. In addition, two intelligent optimization algorithms that are already more mature: the particle swarm algorithm and the genetic algorithm are added to compare with the above three algorithms. The accuracy of neural network prediction before and after the optimisation of neural network by seagull algorithm, the butterfly algorithm incorporating reverse learning, the artificial rabbit algorithm, the particle swarm algorithm, and the genetic algorithm are got through the results respectively. The results of the experiments displayed that the neural network optimized of the improved butterfly algorithm incorporating reverse learning has a prediction accuracy of 99.69 % with stable results, the optimised neural network prediction accuracy of the seagull algorithm reaches

关键词： intelligent algorithm Neural networks Ultra-wideband antennas Weights and biases

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Power allocation in cognitive radio networks over Rayleigh-fading channels with hybrid intelligent algorithms

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WIRELESS NETWORKS 2018年第7期24卷 2397-2407页

作者： Li, Feng Lam, Kwok-Yan Wang, Li Zhejiang Univ Technol Coll Informat Engn 288 Liuhe Rd Hangzhou Zhejiang Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

In this paper, we introduce a hybrid strategy which combines pattern search (PS) optimization and genetic algorithm (GA) to address the problem of power allocation in cognitive radio networks. Considering the fluctuating interference thresholds in cognitive networks, an approach for promoting the coexistence of licensed users and cognitive users is designed. Secondly, based on the analysis of transmission outage probability, a corresponding objective function with regard to the power allocation over Rayleigh fading channels is obtained. It is a difficult task to obtain this objective function directly by using traditional methods, such as common mathematical deduction or linear programming, due to the nonlinearity and complexity of the underlying optimization problem. Inspired by the concept of intelligent algorithms, we employ the scheme of combining PS optimization and GA method, which are both efficient intelligent algorithms to address this challenge. The advantage of this hybrid strategy is that it can overcome the instability problem of GA as well as the local convergency problem of PS method. Thus, the hybrid intelligent method can attain a global and steady outcome. We improve the performance of power allocation strategy with an acceptable increase in computation overhead. The numerical results are encouraging and show that the proposed approach is worthy of consideration in achieving complicated power optimization. Hence, we achieve steady and rational outcomes by applying the proposed hybrid strategy when traditional method is to be ineffective in addressing the nonlinear objective.

关键词： Cognitive radio (CR) intelligent algorithm Power allocation Outage probability

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