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Rapid quality evaluation of moutan cortex (Paeonia suffruticosa Andrews) by near-infrared spectroscopy and bionic swarm intelligent optimization algorithm

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JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS 2025年 260卷 116822页

作者： Qiao, Ying Kang, Yatong Long, Tingze Yi, Han Wang, Feng Chen, Chao Guangdong Pharmaceut Univ Sch Tradit Chinese Med Guangzhou 510006 Peoples R China Key Lab Digitalizat Qual Evaluat Chinese Mat Med S Guangzhou 510006 Peoples R China Res Ctr Qual Engn & Technol Chinese Mat Med Guangd Guangzhou 510006 Peoples R China Guangdong Pharmaceut Univ Guangdong Prov Key Lab Res & Evaluat Pharmaceut Pr Guangzhou 510006 Peoples R China Guangdong Pharmaceut Univ Sch Pharm Guangzhou 510006 Peoples R China

Moutan Cortex (MC), recognized as a traditional Chinese medicinal herb, possesses significant therapeutic properties. The existing quality assessment method only measures the content of one component in MC, which is obviously not comprehensive enough. Besides, the determination process is time-consuming and ***, this article presents a novel approach for the rapid, precise, and efficient quality assessment of MC based on near-infrared spectroscopy (NIR) technology in combination with the bionic swarm intelligent optimization algorithms. First, MC samples were collected and acquired with the NIR spectra in diffuse reflectance mode. Second, the content of paeonol, paeoniflorin, and gallic acid in MC was determined by high-performance liquid chromatography, and the content of total flavonoids and phenols was determined by UV-visible spectrophotometry. Afterward, all the measured content was analyzed in correlation with the NIR spectra of MC, and the partial least squares regression method was utilized to build the models. Especially, to improve the models' performance, five famous bionic swarm intelligent optimization algorithms were investigated to perform the wavelength selection. As a result, the models' performance was significantly enhanced. The coefficient of determination (R-2) > 0.9 and residual prediction deviation (RPD) > 3 were observed on the calibration set and the prediction set. Thus, we believe that bionic swarm intelligent optimization algorithms have the potential to enhance the performance of quantitative models considerably, which offers substantial support for the quality assessment of MC and shows promising applications in the domain of NIR analysis.

关键词： swarm intelligent optimization algorithm Near-infrared spectroscopy Chemometrics Moutan Cortex Quality evaluation

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Optimal design of 10-bit single-slope ADC for CMOS image sensor based on swarm intelligent optimization algorithm

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IET CIRCUITS DEVICES & SYSTEMS 2021年第8期15卷 787-802页

作者： Ma, Yan-Hua Li, Ji-Tong Zhu, Ming Chang, Yu-Chun Dalian Univ Technol Sch Microelect Dalian Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China DUT Artificial Intelligence Inst Dalian Peoples R China

As one of the most critical blocks of the CMOS image sensor (CIS), the accuracy and power consumption of the analogue-to-digital converter (ADC) play an important role in its performance. However, due to the mutually restrictive relationship between power consumption and accuracy in the single-slope ADC (SS ADC), it is difficult to improve these performance indexes meantime without an efficient optimal design method. Here, an optimal design methodology based on an improved artificial fish swarm optimization algorithm (IAFSOA) is proposed for a 10-bit SS ADC in CIS. First, a voltage storage structure and offset calibration circuit are developed for promoting the performance of the comparator. Then, IAFSOA is developed for the circuit parameters optimization with faster convergence rate and higher computational accuracy. The proposed multi-objectives optimization algorithm can obtain the optimal parameters of the kernel circuit components and lead to an enhancement of both power consumption and accuracy. The optimized SS ADC is designed based on 0.18 mu m process to verify the effectiveness of the proposed method. Compared to the earlier reported work, the IAFSOA-based optimal design method can promote the performance of the SS ADC by an improvement of effective number of bits and a reduction of power consumption.

关键词： particle swarm optimisation multiobjectives optimization algorithm low-power electronics kernel circuit components optimized SS ADC size 0.18 mum optimisation calibration circuit performance indexes circuit parameters optimization swarm intelligent optimization algorithm CIS IAFSOA-based optimal design method CMOS image sensor calibration higher computational accuracy critical blocks 10-bit SS ADC efficient optimal design method CMOS image sensors 10-bit single-slope ADC voltage storage structure improved artificial fish swarm optimization algorithm power consumption optimal parameters optimal design methodology analogue-to-digital converter analogue-digital conversion mutually restrictive relationship

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A New Analysis Framework for Solving Multiple Frequencies and Solutions of Nonlinear Piezoelectric Energy Harvesters

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 140卷

作者： Yang, Yi Xiang, Hongjun Beijing Jiaotong Univ Inst Smart Mat & Struct Sch Civil Engn 3 Shangyuan Village Beijing 100044 Peoples R China Beijings Key Lab Struct Wind Engn & Urban Wind Env Beijing 100044 Peoples R China

Nonlinear piezoelectric energy harvesters (NPEHs) have garnered significant interest due to their wide harvesting bandwidth and high efficiency. Nevertheless, their inherent complexity introduces design challenges. The Harmonic Balance Method (HBM) and the Sweep Method (SM) are commonly employed analytical tools;however, both have limitations in generating accurate frequency response curves (FRCs). Combining these methods leverages their respective strengths. The swarm intelligent optimization algorithm (SIOA) effectively identifies all roots in the nonlinear algebraic equations derived from HBM. Further advancements have been achieved with the Enhanced Exploration-Exploitation SIOA (E3SIOA), which significantly improves performance. The proposed E3SIOA-based SM-HBM analysis framework has led to the discovery of previously unidentified nonlinear dynamical phenomena, such as isolated high-energy orbits (HEOs) and the "broken band" effect. These newly identified phenomena, previously overlooked, have substantial implications for enhancing energy harvesting efficiency.

关键词： Nonlinear piezoelectric energy harvester Harmonic balance method Sweep method swarm intelligent optimization algorithm

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Ship route planning based on intelligent mapping swarm optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期176卷

作者： Ma, Weihao Han, Yueyi Tang, Huan Ma, Dongfang Zheng, Huarong Zhang, Yang Zhejiang Univ Ocean Coll Zhoushan Peoples R China Zhejiang Univ Hainan Inst Sanya 572025 Peoples R China Zhejiang Univ Engn Res Ctr Ocean Sensing Technol & Equipment Zhoushan 316021 Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang Pr Zhoushan Peoples R China Wuhan Elect Power Tech Coll Wuhan Peoples R China Zhejiang Univ Ocean Coll Zhoushan Campus Zhihai Bldg 2121 Zheda Rd Zhoushan 316021 Peoples R China

In this paper, we propose a new strategy for simultaneously optimizing ship route and speed using a hierarchical mapping method that takes into account different carbon tax models. Compared with the traditional route planning methods, the main novelty of the proposed method is to separate the decision-making and weather information layers, which allows us to directly obtain the ship route and speed decision plan that conform to the ship's maneuverability and crew habits. The key algorithmic contribution is a bi-layer mapping intelligent optimization algorithm, which establishes the mapping relationship between the upper and lower layers and a synchronous iterative update strategy. Case studies show that the designed method has significant advantages in both conventional and complex terrain ocean scenarios. Moreover, the sensitivity analysis showed that the proposed method can help shipping companies to better cope with rising fuel prices and various ship carbon tax models.

关键词： Ship route planning Bi-layer mapping swarm intelligent optimization algorithm

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Energy-saving access point configurations in WLANs: a swarm intelligent approach

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19332-19364页

作者： Chen, Long Xu, Fangyi Jin, Kezhong Tang, Zhenzhou Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zhejia Jinhua 321000 Peoples R China Wenzhou Univ Key Lab Intelligent Networking Wenzhou City Wenzhou 325000 Peoples R China

Wireless local area networks (WLANs) bring great convenience for people, however, they also consume a huge amount of energy, for most access points (APs) typically operate at the maximum transmit (TX) power all day. In this connection, this paper aims to minimize the overall TX power of all APs by jointly optimizing the location, state and TX power of each AP on the premise of full effective coverage. This paper considers not only three-dimensional scenarios with various obstacles, but also the fluctuation of user density in different time periods and different coverage intensity requirements. To solve the above problem, this paper proposes an improved elite golden jackal optimization (GJO) algorithm, named IEGJO, by introducing the global search strategy and elite evolution strategy into GJO. The performance of IEGJO was extensively evaluated and compared with eight state-of-the-art heuristic algorithms on 20 popular benchmark functions. The experimental results indicate that the IEGJO algorithm outperforms other algorithms in terms of comprehensive performance and ranks first. Then, this paper develops optimal AP configurations method based on IEGJO and applies it to optimize a WLAN in a campus building. The simulation results show that the total TX power of the system is reduced by 81.33%, while still guaranteeing the full effective coverage requirements. The source code is available on https://***/iNet-WZU/IEGJO.

关键词： Energy-saving optimization of WLAN User density The working status of APs Golden Jackal optimization swarm intelligent optimization algorithm

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Thermal positioning error modeling of machine tools using a bat algorithm-based back propagation neural network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期97卷 2575-2586页

作者： Li, Yang Zhao, Ji Ji, Shijun Jilin Univ Sch Mech Sci & Engn Changchun 130025 Jilin Peoples R China

Thermal error of a machine tool is one of the main reasons affecting the machining accuracy. Heat production and heat transfer of a machine tool are too complicated to predict the generated thermal error accurately. According to the nonlinear and time-varying characteristics of thermal error, the back propagation (BP) neural network is perfectly suitable for thermal error modeling, which has been extensively used to map the nonlinear relationship. However, traditional BP neural network usually has poor prediction performance under different operating conditions. Therefore, a new swarm intelligent optimization algorithm, bat algorithm (BA), is introduced to optimize BP neural network and improve its performance. The focus of this paper is the application of the combined algorithm (bat algorithm-based back propagation neural network) to solve the problem of thermal error modeling. Thermal positioning error experiments were conducted on a three-axis experiment bench. The experimental results show that thermal positioning error model built by BA-BP neural network is more stable and has high prediction accuracy and strong robustness, which can provide a candidate method for thermal error modeling.

关键词： Thermal error modeling Bat algorithm BP neural network CNC machine tools swarm intelligent optimization algorithm

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An Adaptive Whale optimization algorithm Integrating Multiple Improvement Strategies 7

An Adaptive Whale Optimization Algorithm Integrating Multipl...

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7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Mai, Weifeng Mo, Liping Jishou Univ Sch Informat Sci & Engn Jishou 416000 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

A self-adaptive whale optimization algorithm integrating four improvement strategies is proposed to overcome the shortcomings of the basic whale optimization algorithm that are easy to fall into local optima, slow convergence speed and low calculation accuracy. Firstly, the chaotic logistics mapping method is used to initialize the population randomly to increase the diversity of the population and the uniformity of individual distribution. Secondly, the convergence factor is non-linearized and dynamically adjusted to balance the global search and local search performance of the algorithm. Thirdly, the dynamic adjustment of the search step length is realized by setting different adaptive inertia weights according to stages. Finally, crossover and mutation operations are performed on the random dimensions of the population individuals to avoid the algorithm from falling into the local optimum. The results of comparative experiments with three related algorithms on six benchmark functions show that the proposed algorithm is superior to the others in terms of global search capability, convergence speed and calculation accuracy.

关键词： swarm intelligent optimization algorithm whale optimization algorithm genetic algorithm chaotic mapping self-adaptation

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swarm Intelligence-Based Performance optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

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IEEE ACCESS 2019年 7卷 161524-161553页

作者： Cao, Li Cai, Yong Yue, Yinggao Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang 44163 Peoples R China

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet peoples requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend

关键词： optimization Wireless sensor networks Internet of Things Particle swarm optimization Classification algorithms Monitoring Reliability Internet of things mobile wireless sensor networks swarm intelligent optimization algorithm performance optimization multi-objective optimization energy efficiency reliability

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An Adaptive Fruit Fly optimization algorithm for optimization Problems

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Journal of Applied Mathematics and Physics 2023年第11期11卷 3641-3650页

作者： L. Q. Zhang J. Xiong J. K. Liu College of Computers and Engineering Chongqing Three Gorges University Chongqing China College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

关键词： swarm intelligent optimization algorithm Fruit Fly optimization algorithm Adaptive Step Local Optimum Convergence Speed

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Parameter estimation for fractional-order nonlinear systems based on improved sparrow search algorithm

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第10期35卷

作者： Zhou, Yongqiang Yang, Renhuan Chen, Yibin Huang, Qidong Shen, Chao Yang, Xiuzeng Zhang, Ling Wei, Mengyu Wuyi Univ Fac Intelligent Mfg Jiangmen 529020 Peoples R China Guangxi Normal Univ Nationalities Dept Phys & Elect Engn Chongzuo 550001 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Chinese Acad Sci R P China Guiyang 510632 Peoples R China Univ Macau Fac Sci & Technol Macau 999078 Peoples R China

Parameter estimation is important in the study of control and synchronization of fractional-order nonlinear systems (FONSs). This paper proposes an improved Sparrow Search algorithm (ISSA) for the parameter estimation problem of FONSs. The algorithm improves the population initialization, position update method of discoverers and warning sparrows based on Sparrow Search algorithm (SSA), and the parameter estimation simulation experiment for fractional-order financial nonlinear system and fractional-order L nonlinear system is conducted to demonstrate this method. The experimental results show that the proposed ISSA is superior to the SSA, Particle swarm optimization (PSO), Whale optimization algorithm (WOA) and Harris Hawks optimization (HHO) in terms of parameter optimization accuracy and convergence speed, which validates the advantages of the ISSA.

关键词： Fractional-order nonlinear systems parameter estimation improved sparrow search algorithm swarm intelligent optimization algorithm

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