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Retrieval of particle size distribution from aerosol optical thickness using an improved particle swarm optimization algorithm

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OPTICAL REVIEW 2015年第5期22卷 809-818页

作者： Mao, Jiandong Li, Jinxuan Beifang Univ Nationalities Sch Elect & Informat Engn Yinchuan 750021 Peoples R China

particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.

关键词： Aerosol particles particle swarm optimization algorithm particle size distribution Simple genetic algorithm Aerosol optical thickness

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Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2015年第5期5卷 677-685页

作者： Li, Juelong Zhang, Xun Xing, Jianchun Wang, Ping Yang, Qiliang He, Can PLA Univ Sci & Technol Coll Def Engn Nanjing Peoples R China Res Ctr Coastal Def Engn Beijing Peoples R China

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cablestayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

关键词： Cable-stayed bridge Structural health monitoring Cumulative effective modal mass particle swarm optimization algorithm Optimal sensor placement

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Network Scheduling Model of Cloud Computing based on particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2015年第4期8卷 73-81页

作者： Lu, Ke Meng, Junxia Jiaozuo Univ Dept Int Educ Jiaozuo 454003 Peoples R China Jiaozuo Univ Coll Informat Engn Jiaozuo 454003 Peoples R China

The paper proposed a network scheduling in cloud computing based on intelligence particle swarm optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence particle swarm optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved particle swarm optimization algorithm, and found out the cloud computing network scheduling and the algorithm's global optimal solution through improved particle swarm optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence particle swarm optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution's time of objective function value is shorten which meet the user's needs more.

关键词： Network Computing Cloud Computing particle swarm optimization algorithm Network Scheduling

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Weighted particle swarm optimization algorithm for Randomized Unit Testing 1

Weighted Particle Swarm Optimization Algorithm for Randomize...

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IEEE International Conference on Electrical, Computer and Communication Technologies

作者： Dhivya, K. Devika Rani Meenakshi, V. S. Sri Krishna Arts & Sci Coll Dept CA & SS Coimbatore Tamil Nadu India Dr Mahalingam Coll Engn & Technol Dept Comp Applicat Pollachi India

ISBN: (纸本)9781479960859

Randomized testing is an effective method for testing software units. Thoroughness of randomized unit testing is according to the settings of optimal parameters. Randomized testing uses randomization for some aspects of test input data. Designing Genetic algorithm (GA) is somewhat of a black art. The feature subset selection (FSS) tool is used with GA to assess and to reduce the size and the content of the test case. FSS can be used to find and remove unnecessary parts of the search control automatically. The existing system does not cover all test data in test cases for the reason that it can quickly generate many test cases and does not consider the target method. Thus GA for Randomized unit testing has not achieves high coverage and does not produce better optimal test data. In the proposed method, particle swarm optimization (PSO) algorithm is used for randomized unit testing. PSO algorithm is used to evaluate the target method solutions for test coverage in test data. The main goal is to generate the optimal test parameter, to reduce the size of test case generation and to achieve high coverage of the units under test. PSO achieves high coverage and produce optimal value. PSO algorithm is enhanced weighted value. Weighted particle swarm optimization (WPSO) algorithm uses weight value in calculating the mean best position for each particle. It improves the efficiency of the system and achieves high coverage of the units under test within 5% of the time with better accuracy.

关键词： Randomized unit testing Genetic algorithm Feature Sub Set Selection particle swarm optimization algorithm Weighted particle swarm optimization algorithm

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State Variable Filter Design Using Improvised particle swarm optimization algorithm

State Variable Filter Design Using Improvised Particle Swarm...

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International Conference on Artificial Intelligence and Evolutionary algorithms in Engineering Systems (ICAEES)

作者： Indoria, Aakash Varrun, Varatharajan Akshay Reddy, Murali Krishna Sathyasai, Tejaswi Anand, Baskaran Devi, Nirmala M. Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amrita Sch Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9788132221357;9788132221340

State variable filter design using particle swarm optimization algorithm proves to be better when compared to the conventional design method. It gives several solutions to the component values which are useful in designing the state variable filter. The automatic termination technique gives the best possible solution in lesser time. This technique has several advantages in terms of a quicker convergence rate and efficient computation toward the suitable output, where an added advantage gives the user a control over the output's precision. The performance parameter here can be defined as the trade-off between the convergence time and accuracy of the resulting solution, which is determined by the precision value. The results also indicate that the solution with a predefined precision level can be obtained with the minimum number of iterations in minimum time.

关键词： particle swarm optimization algorithm Real-world optimization problems Complex optimization problems PSO algorithm

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P2P Traffic Identification Using Support Vector Machine and Cuckoo Search algorithm Combined with particle swarm optimization algorithm 1

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3rd CCF Internet Conference of China (CCF ICoC)

作者： Ye, Zhiwei Wang, Mingwei Wang, Chunzhi Xu, Hui Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (数字)9783662468265

ISBN: (纸本)9783662468265;9783662468258

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

关键词： P2P traffic identification Support vector machine Cuckoo search algorithm particle swarm optimization algorithm

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A Novel Quantum-behaved particle swarm optimization algorithm 14

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Zhao, Jing Liu, Hong Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Jinan Peoples R China Shandong Normal Univ Shandong Prov Key Lab Distributed Comp Software Coll Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781467365932

A novel Quantum-behaved particle swarm optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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Plasma Breakdown optimization Calculation Based on Improved particle swarm algorithm for TT-1 Device

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JOURNAL OF FUSION ENERGY 2024年第2期43卷 24-24页

作者： Shu, Shuangbao Zhang, Jiaxin Zhang, Shurui Luo, Jiarong Liu, Shanlin Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China Chinese Acad Sci Inst Plasma Phys Hefei 230031 Peoples R China

In the Tokamak discharge experiment, obtaining the largest possible null field region is a necessary condition for the smooth breakdown of the plasma, and adjusting the poloidal field coil current is key to achieving a better null field region. This paper, based on the Sino-Thai Tokamak cooperation project Thailand Tokamak-1 (TT-1) device, employs an exponentially decreasing particle swarm optimization (PSO) algorithm to optimize the poloidal field coil current to create the desired null field region in the vacuum chamber area. First, a calculation model for the mutual inductance coefficient and the null field region is established according to the characteristics and magnetic structure of the TT-1 device, enabling the calculation of the null field region. Then, an optimization model for the poloidal field coil current is established, aiming to create a sufficiently large null field region (less than 10 Gauss) to facilitate breakdown. The optimization is carried out using both a typical linearly decreasing PSO algorithm and an improved PSO algorithm to determine the optimal poloidal field coil current. Compared to the unmodified PSO algorithm, the improved PSO algorithm reduces the root mean square error by 31.80%. The results show that the improved PSO algorithm is more suitable for the optimization of the poloidal field coil, has stronger optimization capabilities, and can effectively create the desired null field region, providing an important reference for the smooth breakdown of plasma in the TT-1 device.

关键词： Tokamak device Mutual inductance coefficients Null field region The poloidal field coil current optimization particle swarm optimization algorithm

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An IoT-based English translation and teaching using particle swarm optimization and neural network algorithm

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SOFT COMPUTING 2023年第19期27卷 14431-14450页

作者： Zhang, Lili Anhui Univ Finance & Econ Sch Languages & Media Bengbu 233000 Anhui Peoples R China

English language translation teaching plays a crucial role in today's globalized world, particularly in countries like China, which has seen an increased emphasis on English language proficiency. However, effective teaching and assessment of students' translation abilities remain significant challenges for educators. To address the challenges of English language translation and teaching, this paper proposes a novel approach that integrates Internet of Things (IoT), particle swarm optimization (PSO), and Neural Network algorithms to assist teachers in evaluating and assessing students' English translation abilities by enabling them to better fulfill their teaching responsibilities. This paper first describes the computational mechanism of Neural Network algorithm using PSO, which includes the particle coding approach. An application model is designed to assess students' capacity for learning using English translational instruction. The PSO algorithm gathers information on their learning progress, and fully utilize the analyzed results to develop learning strategies and create teaching materials for various learning types, which is helpful for the quick advancement of English translation teaching. Secondly, this paper develops a translational application model for English language as a practical framework. The proposed method utilizes the upgraded PSO-enabled Neural Network to process English language translation and teaching data by achieving network training in a global optimal state of PSO by reducing training errors. Thirdly, this paper evaluates the effectiveness of the model by comparing the average errors of the training and test samples with different numbers of particles such as 5, 10, and 20. Finally, the results demonstrate high accuracy in measuring students' translation abilities. Furthermore, the study collects and examines three data sets, specifically English-Foreign Language Translation Corpus (EFLT), English Language Learners' Corpus (ELLC), and Engl

关键词： Internet of Things particle swarm optimization algorithm Neural network Network training English translation Smart classrooms Smart services

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Simulating particle swarm optimization algorithm to Estimate Likelihood Function of ARMA（1, 1） Model

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Journal of Mathematics and System Science 2015年第10期5卷 399-410页

作者： Basad Ali Hussain Al-sarray Laboratoire de Mathematiques de Besancon Universite de Franche-Comte Besancon cedex France.

This paper present a simulation study of an evolutionary algorithms, particle swarm optimization PSO algorithm to optimize likelihood function of ARMA（1, 1） model, where maximizing likelihood function is equivalent to maximizing its logarithm, so the objective function ＇***＇ is maximizing log-likelihood function. Monte Carlo method adapted for implementing and designing the experiments of this simulation. This study including a comparison among three versions of PSO algorithm “Constriction coefficient CCPSO, Inertia weight IWPSO, and Fully Informed FIPSO”, the experiments designed by setting different values of model parameters al, bs sample size n, moreover the parameters of PSO algorithms. MSE used as test statistic to measure the efficiency PSO to estimate model. The results show the ability of PSO to estimate ARMA＇ s parameters, and the minimum values of MSE getting for COPSO.

关键词： particle swarm optimization algorithm Likelihood function ARMA（1 1） Model

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