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检索条件"主题词=Particle Swarm optimization algorithm"

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A Novel Inventory and Distribution Planning Model with Non-conforming Items Disposal Under Fuzzy Random Environment 10th

A Novel Inventory and Distribution Planning Model with Non-c...

引用

10th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Kang, Kai Pu, Wei Ma, Yanfang Wei, Xuguang Hebei Univ Technol Sch Econ & Management Tianjin Peoples R China

ISBN: (纸本)9789811018374;9789811018367

This paper investigates a novel inventory and distribution planning model with non-conforming items disposal (NIDPNCID) under fuzzy random environment to minimize the whole process cost. In this process, a certain fraction or a random number of produced items are defective. These non-conforming items are rejected in order to improve the consumer satisfaction. To solve the problem, a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm with fuzzy random simulation is proposed, which can be easy to implement. In more specific terms, DP-based PSO can reduce the dimensions of a particle by using the state equation, which significantly reduced the solution space.

关键词： Inventory Non-conforming items Distribution Fuzzy random particle swarm optimization algorithm

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Classifying DNA Microarray for Cancer Diagnosis via Method Based on Complex Networks 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Wu, Peng Dong, Likai Fan, Yuling Wang, Dong Univ Jinan Sch Informat Sci & Engn Jinan Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (数字)9783319633121

ISBN: (纸本)9783319633121;9783319633114

Performing microarray expression data classification can improve the accuracy of a cancer diagnosis. The varying technique including Support Vector Machines (SVMs), Neuro-Fuzzy models (NF), K-Nearest Neighbor (KNN), Neural Network (NN), and etc. have been applied to analyze microarray expression data. In this investigation, a novel complex network classifier is proposed to do such thing. To build the complex network classifier, we tried a hybrid method based on the particle swarm optimization algorithm (PSO) and Genetic Programming (GP), of which GP aims at finding an optimal structure and PSO accomplishes the fine tuning of the parameters encoded in the proposed classifier. The experimental results conducted on Leukemia and Colon data sets are comparable to the state-of-the-art outcomes.

关键词： DNA microarray data Complex network classifier particle swarm optimization algorithm Genetic Programming

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Two-Level Intellectual Classifier Based on the SVM algorithm 6

Two-Level Intellectual Classifier Based on the SVM Algorithm

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6th Mediterranean Conference on Embedded Computing (MECO)

作者： Demidova, Liliya Sokolova, Yulia Ryazan State Radio Engn Univ Gagarin Str 59-1 Ryazan 390005 Russia

ISBN: (纸本)9781509067428

In this paper the two-level intellectual classifier based on the SVM algorithm has been offered. This classifier works as the group of the SVM classifiers at the first level and as the final SVM classifier on the base of the modified PSO algorithm at the second level. Herewith, we use the original experimental dataset to develop the different SVM classifiers at the first level, and the new dataset, containing only supports vectors of the SVM classifiers of the first level, to develop the final SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered two-level intellectual classifier.

关键词： SVM classifier support vectors kernel function type kernel function parameters regularization parameter particle swarm optimization algorithm

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Galactic swarm optimization using Artificial Bee Colony algorithm 15

Galactic Swarm Optimization using Artificial Bee Colony Algo...

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15th International Conference on ICT and Knowledge Engineering (ICT&KE)

作者： Kaya, Ersin Babaoglu, Ismail Kodaz, Halife Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

ISBN: (纸本)9781538621158;9781538621172

Galactic swarm optimization (GSO) algorithm is a novel meta-heuristic algorithm inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. The GSO algorithm utilizes multiple cycles of exploration and exploitation in two levels. The first level covers the exploration, and different subpopulations of the candidate solutions are used for exploring the search space. The second level covers the exploitation, and best solutions obtained from the subpopulations are considered as a superswarm and used for exploiting the search space. The first implementation of GSO algorithm was presented by using particle swarm optimization algorithm (PSO) algorithm on both first and second levels. This study presents the preliminary results of an implementation of GSO algorithm by using artificial bee colony (ABC) algorithm on the first level and PSO algorithm on the second level. Due to the better exploration characteristics of ABC algorithm over PSO algorithm, this suggestion covers the usage of ABC algorithm on the first level, and the usage of PSO algorithm on the second level. The proposed approach is tested on 20 well-known online available benchmark problems and preliminary results are presented. According to the experimental results, the proposed approach achieves more successful results than the basic GSO approach.

关键词： galactic swarm optimization artificial bee colony algorithm particle swarm optimization algorithm meta-heuristic

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CT to Ultrasound Registration for Non-Invasive Kinematic Analysis of Knee Joints 4

CT to Ultrasound Registration for Non-Invasive Kinematic Ana...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Li, Meng Huang, Qinghua Li, Wenrui Li, Jianyi South China Univ Technol Dept Elect & Informat Engn Guangzhou Guangdong Peoples R China Southern Med Univ Dept Anat Guangdong Prov Key Lab Med Biomech Sch Basic Med Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538611074

Accurate measurement of the motion of individual bones in the knee joint is extremely important for the recovery of patients with motor dysfunction. The most recognized clinical method for measuring is inserting tantalum beads into the bone act as landmark prior to imaging using X-ray equipment at present. Nevertheless, the approach is invasive and the exposure to ionizing radiation is inevitable. This paper puts forward a new non-invasive, precise 3D kinematic analysis system based on registration of CT-scans and B-mode ultrasonic images which is able to capture the flexion of the bones in the knee joint accurately. The experimental results in this paper indicate that the system is capable of measuring the motion of the bones in the knee joint with high precision which is able to meet the need of 3D kinematic analysis for the knee joint.

关键词： CT-US Registration H-shaped image non-invasive particle swarm optimization algorithm mutual information

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particle swarm algorithm Based Task Scheduling for Many-Core Systems 12

Particle Swarm Algorithm Based Task Scheduling for Many-Core...

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12th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Lu Junliang Hu Wei Shen Huan Li Yaxin Liu Jing Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China

ISBN: (纸本)9781509061617

Task scheduling is one of the key factors that determine the performance of the computer systems. When there are more cores on a single chip, how to schedule the multiple tasks to these cores is still important and also a challenge. In order to optimize the efficiency of task scheduling of many-core computer systems, this paper proposes a new task scheduling algorithm based on particle swarm ant colony algorithm. In this algorithm, the fitness function is chosen according to the system model with many cores and multiple tasks. This algorithm uses B-level scheduling method in the initial stage to generate the initial pheromone distribution. In the iteration process, the improved way is used to update pheromone to adjust operator. At the same time, the crossover and mutation strategy of genetic algorithm is used for adaptive crossover and mutation of local optimum and global optimum particles, which makes the location of particles be able to change and update according to the fitness value of particles. The experimental results show that the improved algorithm is superior to the genetic algorithm in the performance of task scheduling.

关键词： task scheduling particle swarm optimization algorithm many-core

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Handwritten Numeral Recognition with a Quantum Neural Network Model 2

Handwritten Numeral Recognition with a Quantum Neural Networ...

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2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Mao Yaxuan Kazuyuki Aihara Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781509064151;9781509064144

Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks(QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Backpropagation(BP) algorithm and particle swarm optimization(PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.

关键词： Handwritten numeral recognition Quantum neural network Back-propagation algorithm particle swarm optimization algorithm

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Application of Improved Wavelet Neural Network in MBR Flux Prediction 16

Application of Improved Wavelet Neural Network in MBR Flux P...

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16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Cai, Guoshuai Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software 399 Bin Shui Xi Rd Tianjin 300387 Peoples R China

ISBN: (纸本)9781509055074

Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning, the main components of the factors affecting the flux of MBR membrane were analyzed. The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the output. Then, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.

关键词： MBR Membrane flux particle swarm optimization algorithm Wavelet neural network BP neural network

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Application of PSO algorithm with Dynamic Inertia Weight in Medical Image Thresholding Segmentation 19

Application of PSO algorithm with Dynamic Inertia Weight in ...

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19th Annual IEEE International Conference on e-Health Networking, Applications and Services (Healthcom)

作者： LiuNa JiangYan LiShu Hubei Univ Med Ctr Hlth Adm & Dev Studies Shiyan Peoples R China

ISBN: (纸本)9781509067046

Medical image segmentation is an important application in medical image processing. In order to improve the efficiency of Medical image segmentation, a method to select optimal threshold by PSO algorithm with Dynamic Inertia Weight(DW-PSO) is proposed. It makes the proportion of the local and global searching ability can be effectively controlled in the whole process of optimal searching. Compared with PSO algorithm, DW-PSO gets better effects of image segmentation. The data of experiment shows that DW-PSO has obvious advantage in computing efficiency, the precision and stability of searching optimal threshold, as well as the effect of image segmentation.

关键词： particle swarm optimization algorithm Inertia weight Optimal threshold Medical image segmentation

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Intelligent Sign Language Recognition Using Enhanced Fourier Descriptor: A Case of Hausa Sign Language 2

Intelligent Sign Language Recognition Using Enhanced Fourier...

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2nd IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Hassan, S. T. Abolarinwa, J. A. Alenoghena, C. O. Bala, S. A. David, M. Farzaminia, Ali Fed Univ Technol Minna Dept Telecommun Engn Minna Nigeria Univ Malaysia Sabah Fac Engn Kota Kinabalu Malaysia

ISBN: (纸本)9781538608463

Hausa sign language (HSL) is the main communication medium among deaf-mute Hausas in northern Nigeria. HSL is so unique that a deaf-mute individual from other part of the country can rarely understand it. HSL includes static and dynamic hand gesture recognitions. In this paper we present an intelligent recognition of static, manual and non-manual HSL using an enhanced Fourier descriptor. A Red Green Blue (RGB) digital camera was used for image acquisition and Fourier descriptor was used for features extraction. The features extracted chosen manually and fed into artificial neural network (ANN) which was used for classification. Thereafter particle swarm optimization algorithm (PSO) was used to optimize the features based on their fitness in order to obtain high recognition accuracy. The optimized features selected gave a higher recognition accuracy of 90.5% compared to the manually selected features that gave 74.8% accuracy. High average recognition accuracy was achieved;hence, intelligent recognition of HSL was successful.

关键词： Hausa Sign Language Fourier Descriptor particle swarm optimization algorithm Artificial Neural Network

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