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

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Biomechanical study on the nonlinear stiffness behavior of the calf muscle and ground reaction force estimation during running 5

Biomechanical study on the nonlinear stiffness behavior of t...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Jalali, Peyman Varahram, Sina Hassannejad, Reza Ettefagh, Mir Mohammad Univ Tabriz Dept Mech Engn Tabriz Iran

ISBN: (纸本)9781538657034

Understanding the biomechanical behavior of the human body in different conditions of locomotion can be very advantageous for many purposes, such as developing the humanoid robots. With this approach, the aim of this study is investigating the nonlinear stiffness behavior of the calf muscle and the exerted ground reaction force (GRF) during running. For this purpose, a new active model of human body is proposed, which the distinctive features of this model are as follows: 1) The simulation model is considered to be five degrees of freedom (5-DOF) by using the standard solid model to simulate the calf muscle;2) The optimum values of the considered mechanical parameters are obtained using the particle swarm optimization (PSO) algorithm;3) The stiffness of the calf muscle is considered to be nonlinear and active, which is appropriately formulated with regard to the viscoelasticity nature of the calf muscle. This model is tested for two types of hard and soft shoes, and validated by the results of previous studies. Then the effects of different conditions of body mass distribution, touchdown velocities, and different types of shoes on the GRF are investigated and discussed. The results of this study can be used in producing and development of humanoid robots, artificial limbs, etc.

关键词： Calf muscle Ground Reaction Force Active Mass-Spring-Damper model particle swarm optimization algorithm

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SVM Classification: optimization with the SMOTE algorithm for the Class Imbalance Problem 6

SVM Classification: Optimization with the SMOTE Algorithm fo...

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6th Mediterranean Conference on Embedded Computing (MECO)

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

ISBN: (纸本)9781509067428

In this paper a new approach to selection of the optimal parameters values for the SMOTE (Synthetic Minority Over-sampling Technique) algorithm in the problem of the SVM (Support Vector Machine) classification of imbalanced datasets has been suggested. This approach allows reducing the time expenditures for the search of the optimum parameters values of the SMOTE algorithm. The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier.

关键词： sampling imbalanced dataset SVM classifier particle swarm optimization algorithm hybridization

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Research on Path Planning of Automatic Handling Robot 2

Research on Path Planning of Automatic Handling Robot

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2nd International Conference on Information Technology and Intelligent Transportation Systems (ITITS)

作者： Hou, Yuanbin He, Liu Song, Chunfeng Zhang, Yanyan Li, Dong Xian Univ Sci & Technol Sch Elect & Control Engn Yanta Rd 58 Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781614997856;9781614997849

Aiming at the problem that path planning for automatic handling robot in an environment with obstacles, the working environment model of the handling robot is analyzed. And then a path optimization algorithm based on fusing ant colony and particle swarm optimization algorithm is proposed. First of all, this algorithm uses the global search ability of particle swarm to go on a rough search and quickly plans the starting point to the end of the initial path. Then, the pheromone distribution is performed on the initial path. Finally, the ant colony algorithm is used to search the path carefully to get the optimal path. Experimental verification shows that compared with a single ant colony or particle swarm optimization algorithm, fused algorithm about ant colony and particle swarm optimization has a significant improvement in the number of iterations and path planning.

关键词： Handling robot path planning particle swarm optimization algorithm ant colony algorithm

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Optimal Placement of Fault Indicators in Distribution System Using PSO algorithm 43

Optimal Placement of Fault Indicators in Distribution System...

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43rd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Li, Weijian Chen, Wei Guo, Chuangxin Jin, Yu Gong, Xiao Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China China Southern Power Grid Guizhou Power Grid Guiyang Power Supply Bur Guiyang Guizhou Peoples R China

ISBN: (纸本)9781538611272

Recently, fault indicators with communication function have been increasingly used in fault diagnosis of distribution networks. In this paper, an objective function which considers the reliability index of the distribution network and economic factors synthetically has been proposed for optimal placement of fault indicators in distribution networks, and binary particle swarm optimization algorithm is used for solving the optimization problem. The validity of the proposed model is verified by testing on the IEEE-33 node distribution system. The test results show that there is no need to install too many fault indicators in distribution networks and reasonable choice of the number and location of the fault indicator can achieve the comprehensive optimization of distribution network reliability and economy. At the same time, the results and processes of optimization are compared with those obtained by immune algorithm, the result shows that binary particle swarm optimization algorithm is better because of its fast convergence speed and less iteration times, which is an advantage for solving this optimal allocation problem.

关键词： Fault Indicators Optimal Placement particle swarm optimization algorithm

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Optimal Design of Fractional Order Fuzzy PID Controller with Simultaneous Auto-tuned Fuzzy Control Rules and Membership Functions 2

Optimal Design of Fractional Order Fuzzy PID Controller with...

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2nd Conference on swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Chaleshtori, Hengameh Nouraei Mohammadi, Seyed Mohammad Ali Bijami, Ehsan Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781509043309

This paper proposes a new intelligent particle swarm optimization (PSO) based method for design of optimal fractional order fuzzy PID (FOFPID) controller with simultaneous auto-tuned fuzzy control rules and membership functions. In the proposed method the parameters of FOFPID controller including input scaling factors, output scaling factors, fractional order of derivative and integrator, fuzzy rule base and membership functions are considered as tuning parameters and optimized simultaneously using PSO algorithm. Moreover, to reduce the fuzzy system design effort and computational complexity, a novel simultaneous tuning approach is proposed for determining the membership functions and fuzzy rule base. The newly suggested design approach provides a flexible controller with simple structure and straightforward algorithm. To evaluate the effectiveness of the proposed method, the proposed FOFPID controller is applied to solve the Load Frequency Control (LFC) problem in a representative power system with considerations governor saturation and the results are compared to the one obtained by a FOFPID controller with fixed fuzzy part and a fractional order PID (FOPID) controller. Simulation results indicate the superiority of proposed method.

关键词： Fractional order fuzzy PID controller Load frequency control particle swarm optimization algorithm

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Network Intrusion Detection Analysis with Neural Network and particle swarm optimization algorithm

Network Intrusion Detection Analysis with Neural Network and...

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22nd Chinese Control and Decision Conference

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Beijing Automat Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781424451821

Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and particle swarm optimization algorithm (PSOA) is presented in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence, this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. Utilizing the character that rough set can keep the discern ability of original dataset after reduction, the reduces of the original dataset arc calculated and used to train neural network, which increase the detection accuracy. We apply this technique on KDD99 data set and get satisfactory results. The experimental result shows that this intrusion detection method is feasible and effective.

关键词： Neural Network Rough Set Network Intrusion Reduction particle swarm optimization algorithm

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A particle swarm optimization algorithm based on Orthogonal Design

A Particle Swarm Optimization Algorithm based on Orthogonal ...

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2010 IEEE World Congress on Computational Intelligence

作者： Yang, Jie Bouzerdoum, Abdesselam Phung, Son Lam Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781424481262

The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the best particle, without "communicating" with other particles. In our approach, this limitation of the standard PSO is overcome by using a novel crossover operator based on orthogonal design. Furthermore, instead of the "generating-and-updating" model in the standard PSO, the elitism preservation strategy is applied to determine the possible movements of the candidate particles in the subsequent iterations. Experimental results demonstrate that our algorithm has a better performance compared to existing methods, including five PSO algorithms and three evolutionary algorithms.

关键词： algorithm theory elitism preservation strategy evolutionary algorithm evolutionary computation evolutionary strategy generating-and-updating model genetic algorithm multivariate optimization orthogonal design particle swarm optimisation particle swarm optimization algorithm

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Two Sub-swarms Quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy

Two Sub-swarms Quantum-behaved Particle Swarm Optimization A...

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3rd International Symposium on Intelligent Information Technology and Security Informatics

作者： Liu, Zhihua Sun, Hui Hu, Haizhi NanChang Hang Kong Univ Sch Software Nanchang Jiangxi Peoples R China NanChang Inst Technol Dept Comp Sci & Technol Nanchang Jiangxi Peoples R China NanChang Hang Kong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9780769540207

In this paper, a Two Sub-swarms Quantum-behaved particle swarm optimization algorithm Based on Exchange Strategy (TS-QPSO) is proposed. Two sub-swarms of particles with quantum Behavior are set up in TS-QPSO. Once the whole swarm falls into local optima and the best value of the global swarm is not improved after the allowable iterations, the exchange strategy will be carried out. The amount of exchange particles is different in each searching phase. In this way, the population diversity can be improved greatly and the problem that falling into local optima can be avoided effectively. Experiment results show that the overall performance of TS-QPSO is superior to QPSO algorithm and TSPSO algorithm.

关键词： particle swarm optimization algorithm local optima Quantum-behaved particle swarm optimization algorithm exchange strategy

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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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particle swarm optimization algorithm Improvement and Application

Particle Swarm Optimization Algorithm Improvement and Applic...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Xiaoli Baojunjie and Kuanghang Department of Computer Science,Chongqing Education College Chongqing,400067,China

in this paper,through the research of the existing particle swarm optimization algorithm and its improved algorithm,a particle swarm optimization algorithm improvement program is proposed,and the experimental results show that this improved algorithm not only does not increase the complexity,but also has greater improvement in the convergence speed and stability comparing with the original algorithm.

关键词： particle swarm optimization algorithm dynamic weight aggregation population diversity convergence speed

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