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Multi-agent learning mechanism design and simulation of multi-echelon supply chain

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 168卷

作者： Sun, Jun-Yan Tang, Jian-Ming Chen, Zhi-Rui Shaanxi Univ Sci & Technol Fac Mech & Elect Engn Xian 710021 Peoples R China Xian Aerosp Precis Electromech Inst Xian 710010 Peoples R China

A supply chain multi-agent learning mechanism based on the particle swarm optimization algorithm is designed. The manufacturer's profit and the product utility are taken as objective functions to explore the influences of social learning ability and self-learning ability on the manufacturer's optimal price, optimal advertising in-vestment, optimal profit, product utility, and supply chain competition. This research demonstrates the in-fluences of different learning abilities on the evolution of the supply chain. The simulation results show that the most appropriate social learning ability can enhance competition between manufacturers and improve product utility. With the enhancement of self-learning ability, manufacturers have a wider range of pricing, products are more diverse, and consumers have a wider choice of goods. This study shows certain guiding significance for the scientific management of the supply chain and the optimization of the competitive strategy of the enterprises on the chain.

关键词： particle swarm optimization algorithm Multi-echelon supply chain Multi-agent Learning mechanism Simulation analysis

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Multiscale Joint optimization Strategy for Retinal Vascular Segmentation

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SENSORS 2022年第3期22卷 1258-1258页

作者： Yan, Minghan Zhou, Jian Luo, Cong Xu, Tingfa Xing, Xiaoxue Changchun Univ Coll Elect Informat Engn Changchun 130012 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the particle swarm optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

关键词： medical image retinal vascular matching filter multiscale particle swarm optimization algorithm

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Research on Image Segmentation optimization algorithm based on Chaotic particle swarm optimization and Fuzzy Clustering

Research on Image Segmentation Optimization Algorithm based ...

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2018年第七届软件与计算机应用国际会议

作者： Tan Linglong Chen Yehui Li Changkai Electronic communication engineering college Anhui Xinhua University

Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. Chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particle swarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.

关键词： FCM particle swarm optimization algorithm chaotic particle swarm optimization algorithm image segmentation

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Three-phase Volume Rate Inversion of Asphalt Mixture Based on particle swarm optimization 3

Three-phase Volume Rate Inversion of Asphalt Mixture Based o...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Zhang, Bei Xu, Shengjie Zhong, Yanhui Li, Xiaolong Zhengzhou Univ Coll Water Conservancy & Environm Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538662434

Asphalt mixture is the construction material of asphalt pavement surface. Its thickness, compactness and asphalt content are the main indexes of pavement quality evaluation. The LR dielectric model is modified by experiments in this thesis. The modified dielectric model can represent the essential relationship between the composite dielectric properties of asphalt mixture and the three-phase volume ratio of aggregate, asphalt and air voids. Based on the finite-difference time-domain method, the particle swarm optimization (PSO) algorithm for inverse analysis of three-phase volume fraction (air voids, asphalt and aggregate volume fraction) of asphalt mixtures is established by taking the mean square error of actual received signal and analog forward signal as fitness function. Based on this method, the inverse analysis of three-phase asphalt volume fraction mixtures is realized. By comparing the theoretical waveform inversion results with the theoretical model, the feasibility of the algorithm for multi-parameter inversion was verified, and the accuracy of the algorithm was further verified by comparing the inversion results of field measured data with the results of core sampling.

关键词： asphalt mixture dielectric property model three-phase volume ratio particle swarm optimization algorithm inversion algorithm

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optimization procedures for the inversion of impedance spectra to the distribution of relaxation times

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JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年 911卷 116199-116199页

作者： Wang, Chunlin Zhu, Guangyan Zhang, Pengbo Fang, Xiaohong Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Pylon Technol Co Ltd Shanghai 201210 Peoples R China

Tikhonov regularization technology has been applied to solve the ill-posed inversion problem of electrochemical impedance spectra inversion to the distribution of relaxation times (DRT). However, the parameters contributing to the accuracy of the DRT obtained by this method, including types of the EIS data, regularization matrixes, regularization parameters, and shape factors, need sophisticated adjustment. In this work, the influences of these parameters were discussed in detail, and the results showed that the effects of some process parameters on the DRT worked complementarily. Therefore, the DRT inversion program with the particle swarm optimization algorithm (PSO), a multi-objective global optimization algorithm, was developed. It was demonstrated the accuracy, feasibility, and reliability of the proposed DRT program. The program was also applied to separate and quantify the electrochemical processes of the commercial LIBs.

关键词： Electrochemical impedance spectroscopy particle swarm optimization algorithm Distribution of relaxation time Tikhonov regularization

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Prediction of magnetic properties of a single-molecule magnetic metallofullerene cluster DySc2N@C80

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PHYSICA B-CONDENSED MATTER 2022年第0期647卷

作者： Wang, Tong-lun Wang, Wei Li, Qi Li, Bo-chen Shenyang Univ Technol Sch Sci Shenyang 110870 Peoples R China

In this work, we proposed a particle swarm optimization algorithm combined with Monte Carlo simulation method to investigate the magnetic properties of a single-molecule magnetic metallofullerene cluster DySc2N@C80. Based on the experimental results, we estimated the values of the exchange coupling and the anisotropy of the present system according to the critical temperature TC, which achieved a good agreement between simulated and experimental results. In addition, we also obtained the influence of size effect, the curves of magnetization, magnetic susceptibility and internal energy induced by external magnetic field and hysteresis behaviors at different temperatures.

关键词： Single -molecule magnetic metallofullerene cluster Critical temperature particle swarm optimization algorithm Monte Carlo simulation Hyteresis loops

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Performance comparison of the optimized k-section squared-off cascades for enrichment of ¹²⁴Te using two meta-heuristic paradigms

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PROGRESS IN NUCLEAR ENERGY 2022年 145卷 1页

作者： Ezazi, Farzaneh Mallah, Mohammad Hasan Safdari, Jaber Mirmohammadi, Seiedeh Leila Atom Energy Org Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch POB 11365-8486 Tehran Iran

Comparing the performance of square-off cascades with different number of sections and determining the extent of their differences in optimal parameters, can identify more alternatives from an economical point of view reducing the cost of the plant and increasing the incomes due to sales of more products. In this way, this study investigated the performance of optimum square, 2-section squared-off, and 3-section squared-off cascades in the separation of Te-124 to 85% concentration level. The optimization was performed with the particle swarm optimization and Antlion optimization algorithms using an objective function concentrated on maximizing the division factor, D, the total recovery factor, R, and the quantity of total product to be produced. The results obtained for cascades with 160 centrifuges showed that the 3-section squared-off cascade is more efficient than its 2-section counterpart, and both of them have outperformed the square cascade and have a higher recovery factor. It was also shown that PSO results are superior to ALO and can guarantee a better answer to the problem. The results of this study can be a good road map for operational separation of the desired isotope as well isotopes of other elements.

关键词： Squared-off cascade particle swarm optimization algorithm Antlion optimization algorithm Te-124 isotope Recovery factor Separation step

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Optimal design of a BLDC motor using African vulture optimization algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University India

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the Whale optimization algorithm (WOA), Grey Wolf optimization (GWO), Cuckoo Search algorithm (COA), Genetic algorithm (GA), particle swarm optimization (PSO), and the African Vulture optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor. © 2024 The Author(s)

关键词： Brush-less DC motors Cuckoo search algorithm particle swarm optimization algorithm Whale optimization algorithm

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Ensemble approach for mid-long term runoff forecasting using hybrid algorithms

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第11期13卷 5103-5122页

作者： Yue, Zhaoxin Ai, Ping Yuan, Dingbo Xiong, Chuansheng Hohai Univ Coll Comp & Informat Engn Nanjing 211100 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China

Factor selection and model construction play an important role in mid-long term runoff forecasting. Due to the indeterminacy between the data and mid-long term runoff, identifying key factors for mid-long term runoff forecasting is challenging. Another problem for mid-long term runoff forecasting is the low accuracy, which limits practical application. Aiming to solve these problems, an ensemble approach is proposed in this paper. First, we propose a novel method for constructing a comprehensive runoff index, and apply the partial mutual information approach to calculate the correlation between multiple factors and the comprehensive runoff index. Through this calculation, the key factors for the mid-long term runoff forecasting can be selected. Second, we implement mid-long term forecasting by combining improved particle swarm optimization (IPSO) and extreme learning machine (ELM) algorithms, which can improve the accuracy of runoff forecasting. The novelty of the proposed method lies in combining the construction of comprehensive runoff index, the key factor selection and the forecasting model based on IPSO-ELM for mid-long term runoff. Experimental results demonstrate that the proposed forecasting model significantly outperforms the current state-of-the-art of the extreme learning machine algorithms and other classical data-driven models for runoff forecasting in the Yalong River basin. Moreover, the performance for datasets based on different hydrological impact factors in the conducted experiments proves the robustness of the proposed method.

关键词： Comprehensive runoff index Factor selection Extreme learning machine particle swarm optimization algorithm Mid-long term runoff forecasting

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An optimization design strategy of 1-3 piezocomposite ultrasonic transducer for imaging applications

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MATERIALS TODAY COMMUNICATIONS 2020年 24卷

作者： Chen, Dongdong Hou, Chenxue Fei, Chunlong Li, Di Lin, Pengfei Chen, Jun Yang, Yintang Xidian Univ Sch Microelect Xian 740071 Peoples R China

An optimization design strategy based on finite element method (FEM) and particle swarm optimization (PSO) algorithm is developed to fabricate high-performance 1-3 piezocomposite ultrasonic transducer. In this strategy, the structure and performance of ultrasonic transducer are simulated by FEM based PZFlex software to analyze the effect of design parameters (ceramic volume fraction and kerf width) on its performance. Due to the time-consuming of FEM simulation, based on the data obtained by simulations, artificial neural network models are established to describe the mapping relation of the design parameters and performance parameters (center frequency and high electromechanical coupling coefficient). The multi-objective optimization criterion is constructed for ultrasonic transducer, and then the modified PSO algorithm are adopted to optimize design parameters. According to the optimized design parameters, the FEM simulation and experiment are conducted to validate the effectiveness of the developed strategy. The simulation and experiment results are in agreement with the designed performance, which indicates that the developed strategy can be used to optimize design parameters for fabricating high-performance ultrasonic transducer. In addition, the imaging experiments of tungsten wires and RMB coin prove the high imaging performance of the ultrasonic transducer fabricated by the developed strategy.

关键词： 1-3 piezocomposite Ultrasonic transducer Finite element method optimization design particle swarm optimization algorithm

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