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2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Bo Wang Min Zhou Junzo Watada School of Management and Engineering Nanjing University Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781538604854

Economy, reliability and environmental friendly are primary goals when modeling modern unit commitment problems. In this study, we establish a multi-objective unit commitment model considering the above objectives. In particular, the pricing support for ultra-low emissions is addressed together with startup/shutdown, generation and environment concerns when calculating the operation cost of thermal units, which conforms the present situation of power markets, especially in China. To solve the complicated nonlinear model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed on a modified 26-thermal-unit test system, which demonstrates the superiority of this research.

关键词： multi-objective unit commitment problem ultra-low emissions analysis particle swarm optimization algorithm optimization under hybrid uncertainties

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Association Rules Mining Based On Improved PSO algorithm 2

Association Rules Mining Based On Improved PSO Algorithm

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2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shang Qianxiang Wu Ping East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200062 Peoples R China

ISBN: (纸本)9781538620304

One of the most classic algorithms for association rules mining is the Apriori algorithm. But it can't satisfy the requirement as the increasing scale of the data. It has some disadvantages such as scanning database too many times, setting support and confidence thresholds artificially. particle swarm optimization is one of the classic heuristic algorithms and some researchers has used it to association rules mining. But the problem that it may fall into the local optimal solution prematurely affects the efficiency of the algorithm. A new improved particle swarm optimization algorithm is proposed to solve this problem by controlling the particle velocity. In order to improve the efficiency and reliability of the algorithm in the condition of guarantee the global searching capability, an adaptive acceleration coefficient control method based on distance is used.

关键词： association rules Apriori algorithm particle swarm optimization algorithm acceleration coefficients

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Prediction Model of BP Neural Network Based on Improved Genetic algorithm optimization for Infectious Diseases

Prediction Model of BP Neural Network Based on Improved Gene...

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Chinese Automation Congress (CAC)

作者： Ma, Qiufang Xiao, Liqing Qingdao Huanghai Univ Coll Int Elect Commerce Qingdao Peoples R China Huainan Normal Univ Sch Mech & Elect Engn Huainan Peoples R China

ISBN: (纸本)9781538635247

In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized by using the improved genetic algorithm based on the PSO (particle swarm optimization algorithm) while the error function is the mean square error, the mean absolute error and the mean absolute percentage error. The simulation experimental results show that the optimized BP neural network can effectively reduce the mean square error, the mean absolute error and the mean absolute percentage error, and improve the prediction accuracy.

关键词： BP neural network error function particle swarm optimization algorithm genetic algorithm weight and threshold

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RESEARCH AND APPLICATION OF PSO-BASED COMBINED FORECASTING GRAY VERHULST-BP NEURAL NETWORK MODEL 16

RESEARCH AND APPLICATION OF PSO-BASED COMBINED FORECASTING G...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yan, Zi-Jun Wu, Ming-Fen Wuyi Univ Coll Comp Sci Jiangmen 529020 Peoples R China

ISBN: (纸本)9781538604083

The gray Verhulst model has the extremely widespread application in the study of minority, poor information and uncertainty question when the data show saturated state or s-shaped sequences. However the gray Verhulst model built by weakening the randomness of data sequence, lacking of self-organizing and self-learning. Some scholars study on this issue, and put forward a kind of gray Verhulst-BPNN combination forecast model. In this model, Partial-data set is used to establish Verhulst model group and BP neural network is utilized to build up the nonlinear mapping between partial-data Verhulst model group and original data in order to overcome the defects of the neural networking training with small sample of time series data. However, gray Verhulst-BPNN combination forecast model still has the problems of the local minimum and slow convergence caused by adjusting the network connection weights with error back propagation. Considering the PSO algorithm has the advantages of high accuracy and fast convergence, this paper put forward a kind of PSO-based combined forecasting gray Verhulst-BPNN model. Experiments show that the combined model has higher prediction precision and good stability.

关键词： Gray Verhulst model BP-neural network particle swarm optimization algorithm Prediction

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Multiplicate particle swarm optimization algorithm

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JOURNAL OF COMPUTERS 2010年第1期5卷 150-157页

作者： Gao, Shang Zhang, Zaiyue Cao, Cungen Jiangsu Univ Sci & Technol Sch Engn & Comp Sci Zhenjiang 212003 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China

Using particle swarm optimization to handle complex functions with high-dimension it has the problems of low convergence speed and sensitivity to local convergence. The convergence of particle swarm algorithm is studied, and the condition for the convergence of particle swarm algorithm is given. Results of numerical tests show the efficiency of the results. Base on the idea of specialization and cooperation of particle swarm optimization algorithm, a multiplicate particle swarm optimization algorithm is proposed. In the new algorithm, particles use five different hybrid flight rules in accordance with section probability. This algorithm can draw on each other ' s merits and raise the level together The method uses not only local information but also global information and combines the local search with the global search to improve its convergence. The efficiency of the new algorithm is verified by the simulation results of five classical test functions and the comparison with other algorithms. The optimal section probability can get through sufficient experiments, which are done on the different section probability in the algorithms.

关键词： particle swarm optimization algorithm convergence parameter

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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...

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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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