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8th Panhellenic Conference on Artificial Intelligence

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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Grid Task Scheduling Strategy Based on particle swarm optimizationand Ant Colony optimization algorithm

Grid Task Scheduling Strategy Based on Particle Swarm Optimi...

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International Conference on Advanced Measurement and Test (AMT 2010)

作者： Wei Pengcheng Shi Xi Chongqing Univ Comp Sci & Engn Coll Chongqing 630044 Peoples R China Chongqing Educ Coll Dept Comp Sci Chongqing 630044 Peoples R China

ISBN: (纸本)9780878492695

Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimization algorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.

关键词： grid computing task scheduling particle swarm optimization algorithm Ant Colony optimization algorithm

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optimization of Reactive Power Based on Dynamic Learning Factor Multi-objective particle swarm algorithm

Optimization of Reactive Power Based on Dynamic Learning Fac...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Ai, Ying Su, Yixin Peng, Yao Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9783038351153

particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particle swarm optimization algorithm based on dynamic learning factors. The two accelerations are changed with searching stage, so as to enhance the early globle search ability and the late local search ability, then to avoid local optimum;minimum particle angle method and crowded distance method are uesd to determine the global extremum in instalments, so as to improve the convergence speed and accuracy of multi-objective pareto solutions. Take the IEEE 30 bus system IEEE 118 bus system as example, the proposed method is compared with adaptive chaos particle swarm optimization (ACPSO) and NSGA-II, simulation results show that the method put forward in this paper has better convergence accuracy.

关键词： Dynamic Learning Factor particle swarm optimization algorithm Minimum particle Angle Method Crowded Distance Method Reactive Power optimization

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The Study of Characteristics of the Hybrid particle swarm algorithm in Solution of the Global optimization Problem 5

The Study of Characteristics of the Hybrid Particle Swarm Al...

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5th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Klyueva, Irina Pylkin, Alexander Ryazan State Radio Engn Univ Ryazan Russia

ISBN: (纸本)9781509022212

The present paper considers convergence characteristics of the particle swarm algorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and Grid Search algorithm. Comparison of quality indices of the particle swarm algorithm and the steepest descent algorithm has been carried out for evaluation of advantages of the PSO algorithm in comparison with classical optimization algorithms.

关键词： particle swarm optimization algorithm grid search algorithm hybridization solution of problems of the global unconstrained optimization

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Network intrusion detection algorithm based on LightGBM model and improved particle swarm optimization

Network intrusion detection algorithm based on LightGBM mode...

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2024 IEEE Cyber Science and Technology Congress

作者： Geng, Yican Hu, Haoyang Ge, Zhaoxuan Lian, Zhichao Nanjing Univ Sci & Technol Sch Cyber Sci & Engn Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798331532109;9798331532093

In order to solve the problem of insufficient adaptive ability of the network intrusion detection model, the large-scale fast search capability of the particle swarm optimization (PSO) algorithm is introduced into the intrusion detection model. In order to solve the problem that PSO is easy to fall into local optimality, the genetic algorithm (GA) is introduced. An improved particle swarm optimization (GAPSO) algorithm based on genetic algorithm is proposed. This algorithm optimizes the parameters that are difficult to adjust in the lightweight gradient boosting machine (LightGBM) algorithm, so that the PSO algorithm can quickly converge while ensuring the optimization accuracy, and obtain the optimal network intrusion detection model. Experimental results show that GAPSO is more effective than the basic PSO algorithm when dealing with high-dimensional, complex structure optimization problems.

关键词： Supervised learning genetic algorithm particle swarm optimization algorithm intrusion detection parameter optimization

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A Novel Hybrid optimization algorithm Based on Multi-agent and particle swarm

A Novel Hybrid Optimization Algorithm Based on Multi-agent a...

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International Conference on Components, Packaging and Manufacturing Technology

作者： Shi Dejia Jiang Weijin Ding Xiaoling Hunan Univ Commerce Sch Comp & Elect Engn Changsha 410205 Hunan Peoples R China

ISBN: (纸本)9780878492138

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in *** represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of ***. MAPSO realizes the purpose of minimizing the value of objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

关键词： Hybrid optimization algorithm multi-agent system particle swarm optimization algorithm

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particle swarm optimization Least Square Support Machine Based Missing Data Imputation algorithm in Wireless Sensor Network for Nuclear Power Plant's Environmental Radiation Monitor

Particle Swarm Optimization Least Square Support Machine Bas...

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Gao, Song Tang, Yaogeng Qu, Xing South Univ China Inst Elect Heng Yang 421001 Peoples R China

ISBN: (纸本)9783037855447

In wireless sensor network for nuclear power plant's peripheral environmental radiation monitoring the gamma dose rate data may be missed affected by various factors, which will influence the validity of environmental radiation monitoring. To solve the problem, a missing data imputation algorithm is proposed based on particle swarm optimized least squares support vector machine. This algorithm imputes missing data utilizing node's previous monitoring data and neighbor node's current monitoring data jointly. Experimental results using the real radiation monitoring data around a nuclear power plant show that the proposed algorithm can impute the missing gamma dose rate data accurately.

关键词： environmental radiation monitoring wireless sensor network missing data imputation least squares support vector machine particle swarm optimization algorithm

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PSO-TPS: An Optimal Trust Path Selection algorithm Based on particle swarm optimization in Small World Network

PSO-TPS: An Optimal Trust Path Selection Algorithm Based on ...

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2nd International Conference on Cloud and Green Computing / 2nd International Conference on Social Computing and its Applications (CGC/SCA)

作者： Xu, Guangquan Xu, Chao Tian, Xiuming Zhang, Luxia Li, Xiaohong Li, Weisheng Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9780769548647;9781467330275

Small world network is a type of mathematical graph, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. P2P network is a typical small world network, it has a large scale and high risks, so trust model building and Trust Path Selecting (TPS) become big challenges. To solve TPS problem, we first reduce the scale of the complex P2P network by identifying and deleting the equivalent nodes. Then we provide a Trust Path Selection algorithm based on particle swarm optimization (PSO). In the algorithm, after initializing the particle swarm, each particle can update the speed and location according to its information, and then produce a new particle with better value. Repeating that process continually to implement the global search of the space, we can get the better trust path in the networks. The experimental results show that this algorithm is effective and efficient in finding the suboptimal solution of trust path, hence it can be applied in trust path searching in such small-world networks as P2P network.

关键词： Small World Network P2P Network Super Node particle swarm optimization algorithm

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Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8858-8864页

作者： Zhang, Wei-Bo Zhu, Guang-Yu Fuzhou Univ Coll Mech Engn & Automat Fuzhou 35002 Fujian Peoples R China

particle swarm optimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and the other on order exchange, accordingly two versions of PSO algorithms are formed. A new version of PSO algorithm based on order exchange has been presented in our studies, which is capable of converging on the global optimization solution, with the method of generating the stop evolution particle over again. In this paper, we propose another version of PSO algorithm based on value exchange with the same method. There exist, thus, totally four versions of PSO algorithms, which is given a brief introduction individually and the performance of which are compared in solving sequence optimization problems through fifty runs. The performance comparison show that the PSO algorithm with global convergence characteristics based on order exchange outperforms the other versions of PSO in solving sequence optimization problem. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： particle swarm optimization algorithm Sequence optimization Global convergence Hole machining Drilling

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Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第1期27卷 499-515页

作者： Adem, Kemal Hekim, Mahmut Demir, Selim Tokat Gaziosmanpasa Univ Fac Arts & Sci Dept Informat Tokat Turkey Tokat Gaziosmanpasa Univ Fac Engn Dept Elect & Elect Engn Tokat Turkey Tokat Gaziosmanpasa Univ Fac Med Dept Ophthalmol Tokat Turkey

We propose a novel iterative thresholding approach based on firefly and particle swarm optimization to be used for the detection of hemorrhages, one of the signs of diabetic retinopathy disease. This approach consists of the enhancement of the image using basic preprocessing methods, the segmentation of vessels with the help of Gabor and Top-hat transformation for the removal of the vessels from the image, the determination of the number of regions with hemorrhages and pixel counts in these regions using firefly algorithm (FFA) and particle swarm optimization algorithm (PSOA)-based iterative thresholding, and the detection of hemorrhages with the help of a support vector machine (SVM) and linear regression (LR)-based classifier. In the preprocessing step, color space selection, brightness and contrast adjustment, and adaptive histogram equalization are applied to enhance retinal images, respectively. In the step of segmentation, blood vessels are detected by using Gabor and Top-hat transformations and are removed from the image to avoid confusion with hemorrhagic regions in the retinal image. In the iterative thresholding step, the number of hemorrhagic regions and pixel counts in these regions are determined by using an iterative thresholding approach that generates different thresholding values with the FFA/PSOA. In the classification step, the hemorrhagic regions and pixel counts obtained by the iterative thresholding are used as inputs in the LR/SVM-based classifier. PSOA-based iterative thresholding and the SVM classifier achieved 96.7% sensitivity, 91.4% specificity, and 94.1% accuracy for hemorrhage detection. Finally, the experiments show that the correct classification rates and time performances of the PSOA-based iterative thresholding algorithm are better than those of the FFA in hemorrhage detection. In addition, the proposed approach can be used as a diagnostic decision support system for detecting hemorrhages with high success rate.

关键词： Hemorrhage firefly algorithm particle swarm optimization algorithm iterative thresholding

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