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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Cheng, H. J. Ou-Yang, C. Juan, Y. C. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan Ming Chi Univ Technol Dept Ind Engn & Management New Taipei Taiwan

ISBN: (纸本)9781467329453

Information systems have been widely used to support workflow processes to record the execution of tasks in the process and are stored in so-called "event logs". Techniques that relate to events extraction have gotten increasing attention such as process mining techniques. Developed process mining methods such as alpha algorithm, alpha(++) algorithm, and genetic process mining (GPM) are capable of tackling several structures well, but they are still difficult to discover parallelism structures efficiently since the parallelism structures are too complex. This work presents an evolutionary-based process mining approach based on a hybrid of GPM and particle swarm optimization algorithm (PSO) in order to handle parallelism structures. The medical records of acute stroke patients of Taiwanese medical institution are used as a practical case to test the proposed approach. Experimental results on the case show the effectiveness of the proposed approach for tackling parallelism structures.

关键词： Genetic algorithm particle swarm optimization algorithm process mining workflow model

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Research on Intelligence optimization of Web Service Composition for QoS

Research on Intelligence Optimization of Web Service Composi...

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3rd International Conference on Information Computing and Applications (ICICA 2012)

作者： Li, Jianjun Yu, Bin Chen, Wuping Zhengzhou Informat Sci & Technol Inst Zhengzhou 450004 Peoples R China

ISBN: (纸本)9783642340406

With quality of service as the restraint, in accordance with the features of service composition, this paper proposes an intelligent optimization algorithm for Web service composition. By combining a wide search range of shuffled frog leaping algorithm and high accuracy of particle swarm optimization algorithm, this algorithm can find the best one from a lot of service composition schemes. Simulation results show that the algorithm designed by this paper can overcome the low accuracy of shuffled frog leaping algorithm and instability of particle swarm optimization algorithm, and can find the better service composition scheme in all cases.

关键词： Web Service Composition Shuffled Frog Leaping algorithm particle swarm optimization algorithm Intelligent optimization algorithm Quality of Service

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Regional Traffic Control System Study Based on Pattern Identification

Regional Traffic Control System Study Based on Pattern Ident...

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Xia, Baizhan He, Huaiwen Univ Elect Sci & Technol China Zhongshan Inst Zhongshan 528402 Peoples R China

ISBN: (纸本)9783037852828

The thesis introduces traffic patterns definition and identification. Combined with actual project it has established the regional traffic signal coordination and control system based on particle swarm K-means clustering algorithm pattern identification. It puts forward system structure and working principles with discussions focused on several key problems existing in traffic pattern identification process.

关键词： intelligent traffic pattern identification clustering analysis particle swarm optimization algorithm

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optimization of Velocity Loop Parameters in CNC Servo System

Optimization of Velocity Loop Parameters in CNC Servo System

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International Conference on Precision Engineering and Non-Traditional Machining (PENTM 2011)

作者： Yu Bingao Shi Lin Wang Kunqi XIAN Aero Coll Dept Aeronaut Engn Xian 710077 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Sch Mech Elect Engn Xian 710032 Peoples R China

ISBN: (纸本)9783037853184

To optimizing efficiently control parameters in feed servo system for adequately giving out the CNC machine tools, the velocity loop control model is built, and the comprehensive evaluation function is given. The standard particle swarm algorithm is improved to optimizing the parameters of velocity loop control in CNC machine tools. It shows the best overall performance of the velocity loop in CNC machine tools that parameters optimized by improved particle swarm algorithm Compared with genetic algorithm, the standard particle swarm algorithm and handy calculation. This optimized algorithm is a new efficient ways to solve parameter optimization of CNC servo control system.

关键词： particle swarm optimization algorithm CNC velocity loop Parameters optimization

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A Modified League Championship algorithm for Numerical Function optimization Via Artificial Modeling of the "Between Two Halves Analysis"

A Modified League Championship Algorithm for Numerical Funct...

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6th International Conference on Soft Computing and Intelligent Systems (SCIS) / 13th International Symposium on Advanced Intelligence Systems (ISIS)

作者： Kashan, Ali Husseinzadeh Karimiyan, Somayyeh Karimiyan, Morteza Kashan, Mina H. Islamic Azad Univ Dept Syst & Ind Engn Sci & Res Branch Tehran Iran Int Inst Earthquake Engn & Seismol Dept Earthquake Engn Tehran Iran Islamic Azad Univ Dept Earthquake Engn Sci & Res Branch Tehran Iran Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran

ISBN: (纸本)9781467327435;9781467327428

Inspired by the competition of sport teams in a sport league, the League Championship algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/weaknesses/opportunities/threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the postmatch SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed.

关键词： numerical function optimization league championship algorithm particle swarm optimization algorithm

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Feedback Controller Design for Chemotherapy Drug Scheduling via PSO optimization algorithm

Feedback Controller Design for Chemotherapy Drug Scheduling ...

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World Congress on Engineering and Computer Science

作者： Baghernia, P. Moghaddam, R. K. Islamic Azad Univ Mashhad Branch Dept Elect Engn Mashhad Iran

ISBN: (纸本)9789881925244

Cancer is related to a class of diseases characterized by out-of-control cell growth. Chemotherapy as one of the most conventional methods of cancer treatment aims to kill cancer cells, but this treatment will damage healthy cells as well. In this regard, mathematical modeling and optimization of drug scheduling can be effective in improving the drug injection timing, with minimum side effects. In this paper a phase specific cancer tumor model have been considered to describe the effect of drug on different cell populations, plasma drug concentration and toxic side effects. A feedback controller of PID type is developed in order to maintain a predefined drug concentration level. This level or controller's input signal have been determined in such a way as to limit the plasma drug concentration which also limits the toxic side effects. In addition, the particle swarm optimization (PSO) algorithm is employed to optimize the PID controller parameters. Simulation results show that the drug which is injected using this algorithm leads to a reduced number of cancer cells at the end of treatment while the normal cells population, despite the toxicity of the drug, remains almost in the acceptable range.

关键词： Cancer chemotherapy Drug scheduling feedback controller particle swarm optimization algorithm

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optimization Design of Wind Turbine Blades Based On BLADED

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2nd International Conference on Advanced Engineering Materials and Technology (AEMT)

作者： Zheng, Jiahong Ma, Haoran Shaanxi Univ Sci & Technol Inst Elect & Mech Engn Xianyang Peoples R China

ISBN: (纸本)9783037854471

The optimization of wind turbine blades can increase the generator power and annual output of electricity. Illustrated by the case of 2MW wind turbine, optimizing the blade chord and twist angle by POS algorithm. Modeling by BLADED, analysis the change of lift and drag coefficients, the power coefficient, maximum power of wind turbine, minimum power of wind turbine, wind turbine generating capacity before and after optimization. The results show: the aerodynamic efficiency of optimized blade increased by 4.833 than that before optimization. In the wind speed of 12 m/s (that is lower and normal speed), the average power coefficient is improved by 0.05. The minimum power of the wind turbine increased by 4% -15%.The maximum power of the wind turbine increased by 3% -9%. And the annual production of power increased by 0.25%.

关键词： Wind turbine blades particle swarm optimization algorithm optimization design BLADED

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A Improved Clustering Analysis Method Based on Fuzzy C-Means algorithm by Adding PSO algorithm

A Improved Clustering Analysis Method Based on Fuzzy C-Means...

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7th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Pang, Liang Xiao, Kai Liang, Alei Guan, Haibing Shanghai Jiao Tong Univ Sch Software Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9783642289415;9783642289422

Fuzzy c-means algorithm (FCM) is one of the most widely used clustering methods for modern medical image segmentation applications. However the conventional FCM algorithm has certain possibilities of converging to a local minimum of the objective function, thus lead to undesired segmentation results. To address this issue, an improved FCM which is based on clustering centroids updates with the use of particle swarm optimization (PSO) is proposed in this paper. This algorithm is designed to support multidimensional feature data and be accessible through parallel computation. The experimental results suggest that, compared to the conventional FCM algorithm, the proposed algorithm leads to higher chances of global optimum clustering and is less computationally intensive when large clustering number is needed.

关键词： Fuzzy c-means particle swarm optimization algorithm Image segmentation Clustering swarm intelligence

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Application of particle swarm optimization to association rule mining

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APPLIED SOFT COMPUTING 2011年第1期11卷 326-336页

作者： Kuo, R. J. Chao, C. M. Chiu, Y. T. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Business Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

In the area of association rule mining, most previous research had focused on improving computational efficiency. However, determination of the threshold values of support and confidence, which seriously affect the quality of association rule mining, is still under investigation. Thus, this study intends to propose a novel algorithm for association rule mining in order to improve computational efficiency as well as to automatically determine suitable threshold values. The particle swarm optimization algorithm first searches for the optimum fitness value of each particle and then finds corresponding support and confidence as minimal threshold values after the data are transformed into binary values. The proposed method is verified by applying the FoodMart2000 database of Microsoft SQL Server 2000 and compared with a genetic algorithm. The results indicate that the particle swarm optimization algorithm really can suggest suitable threshold values and obtain quality rules. In addition, a real-world stock market database is employed to mine association rules to measure investment behavior and stock category purchasing. The computational results are also very promising. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Association rule mining particle swarm optimization algorithm

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Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8419-8429页

作者： Wang, Jianzhou Chi, Dezhong Wu, Jie Lu, Hai-yan Lanzhou Univ Sch Math & Stat Lanzhou 730000 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14-18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Chaotic time series particle swarm optimization algorithm Trend adjustment

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