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检索条件"主题词=particle Swarm Optimization algorithm"

共 1416 条记录，以下是141-150 订阅

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Performance Evaluation of particle swarm optimization algorithm for Optimal Design of Belt Pulley System

Performance Evaluation of Particle Swarm Optimization Algori...

引用

4th International Conference on swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Sabarinath, Pandurengan Thansekhar, M. R. Saravanan, R. KLN Coll Engn Pottapalayam 630611 India JCT Coll Engn & Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9783319037530;9783319037523

The present scenario in the design of machine elements includes the minimization of weight of the individual components in order to reduce the overall weight of the machine elements. It saves both cost and energy involved. Belts are used to transmit power from one shaft to another by means of pulleys which rotate at the same speed or different speeds. Generally, the weight of pulley acts on the shaft and bearings. In the present study, minimization of weight of a belt pulley system has been investigated. particle swarm optimization algorithm (PSO) is used to solve the above mentioned problem subjected to a set of practical constraints and it is compared with the results obtained by Differential Evolution algorithm (DEA). Our results indicate that PSO approach handles our problem efficiently in terms of precision and convergence and it outperforms the results presented in the literature.

关键词： Optimal Design Belt pulley system particle swarm optimization algorithm

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Study on Passenger Train Stopping Scheme Based on Improved particle swarm optimization algorithm

Study on Passenger Train Stopping Scheme Based on Improved P...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Wang, Shuang Zhao, Peng Qiao, Ke Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

ISBN: (纸本)9781424447541

This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway lines Minimizing the stopping times for all passenger trains, minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the model For a given travel demand and specified capacity of stops, the model is solved by heuristic algorithm An improved discrete particle swarm optimization (PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and stability In the algorithm, a stop based representation is designed, and a new method is used to update the position and velocity of particles In order to keep the particle swarm algorithm from premature stagnation, the simulated annealing algorithm, which has local search ability, is combined with the PSO algorithm to make elaborate search near the optimal solution, then the quality of solutions is improved effectively An empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the algorithm The experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm, and an optimal set of stopping schemes can always be generated for a given demand To achieve the best planning outcome, the stopping schemes should be flexibly planned, and not constrained by specific ones as often set by the planner

关键词： high-speed railway lines passenger train stopping scheme multi-objective programming particle swarm optimization algorithm simulated annealing algorithm

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Discrete particle swarm optimization algorithm for Lot-streaming No-wait Flow Shop Scheduling Problem

Discrete Particle Swarm Optimization Algorithm for Lot-strea...

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

作者： Yang, Lin Pan, Yuxia Hainan Univ Sanya Coll Sanya 572022 Hainan Peoples R China

ISBN: (纸本)9783037854471

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals. After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

关键词： particle swarm optimization algorithm lot-streaming no-wait flow shop scheduling problem local search

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Multi-objective Optimal Sizing Hybrid Power System in a Solar Electric Vehicle Using particle swarm optimization algorithm

Multi-objective Optimal Sizing Hybrid Power System in a Sola...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Zhou Shiqiong Guo Guifang Xiang Yongyang Shenzhen Inst Informat Technol Dept Traff & Environm Shenzhen Peoples R China Tibet Univ Int Informat Engn Dept Tibet Peoples R China

ISBN: (纸本)9783037856932

In a solar electric vehicle, the optimal sizing of hybrid power system can be considered as a multi-objective optimization problem. The two conflicting goals are to maximize the Loss of Peak Power Probability (LPPP) and minimize the system cost. And the former is related to the reliability of the system while the latter relates to whether production prototype so the two optimization objectives are important. An improved particle swarm algorithm was presented to optimal size the hybrid power system. Here the mutation operator of genetic algorithm was introduced and the acceleration factor could change with time. The optimization results show that: the improved particle swarm algorithm can well solve the hybrid power system for multi-objective optimization problems.

关键词： Multi-objective optimal sizing Hybrid power particle swarm optimization algorithm Solar electric vehicle (SEV)

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Fault Localization of Distribution Network Based on Reverse-Local Learning based particle swarm optimization algorithm 3

Fault Localization of Distribution Network Based on Reverse-...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhang, Wei Zhou, Yangjun Yi, Chenying Li, Shan Feng, Bin Huang, Weixiang Pan, Juntao Zhou, Yuanchao Wu, Lifang Guangxi Power Grid Co Ltd Elect Power Res Inst Guangxi Key Lab Intelligent Control & Maintenance Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Peoples R China Guangxi Power Grid Co Ltd Nanning Power Supply Bur Nanning Peoples R China

ISBN: (纸本)9798350375145;9798350375138

With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no longer applicable. To deal with the problems induced by large-scale access of distributed generation, a novel fault localization method based on improved particle swarm optimization algorithm is proposed in this paper. Aiming at the issues of potential misjudge, an objective function/ fitness value correction method is proposed to adapt to bidirectional power flow induced by distributed generation. Aiming at the issues of sluggish convergence and poor robustness in existing models, this paper introduces the Reverse-Local Learning based particle swarm optimization algorithm to improve the optimization efficiency and algorithm stability. Case study is performed on the IEEE-33 bus distribution network to demonstrate the accuracy and efficiency of the proposed fault localization method.

关键词： fault localization distributed generation bidirectional power flow particle swarm optimization algorithm

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Localization Technology Based on Quantum-behaved particle swarm optimization algorithm for Wireless Sensor Network

Localization Technology Based on Quantum-behaved Particle Sw...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Zhao, Ji Fu, Yi Wang, Han-bo Wuxi City Coll Vocat Technol Dept Elect Informat Engn Wuxi 214000 Jiangsu Peoples R China

ISBN: (纸本)9783037855034

This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

关键词： quantum-behaved particle swarm optimization algorithm particle swarm optimization algorithm location wireless sensor networks

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Hybrid particle swarm optimization algorithm for Solving Systems of Nonlinear Equations

Hybrid Particle Swarm Optimization Algorithm for Solving Sys...

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IEEE International Conference on Granular Computing

作者： Ouyang, Aijia Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Math & Comp Sci Nanning 53006 Peoples R China

ISBN: (纸本)9781424448319

A hybrid particle swarm optimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particle swarm optimization (PSO) algorithm, is put forward to solve systems of nonlinear equations, and it can be used to overcome the difficulty in selecting good initial guess for SM and inaccuracy of PSO due to being easily trapped into local optimal. The algorithm has sufficiently displayed the performance of PSO's global searching and SM's accurate local search. Numerical computation results show that the approach has great robust, high convergence rate and precision, it can give satisfactory solutions of nonlinear equations.

关键词： systems of nonlinear equations simplex method particle swarm optimization algorithm

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An effective particle swarm optimization algorithm for flexible job-shop scheduling problem

An effective particle swarm optimization algorithm for flexi...

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5th International Conference on Industrial Engineering and Systems Management (IEEE IESM)

作者： Nouiri, Maroua Jemai, Abderezak Ammari, Ahmed Chiheb Bekrar, Abdelghani Niar, Smail Carthage Univ INSAT Inst MMA Lab Tunis Tunisia King Abdulaziz Univ Fac Engn Jeddah 21413 Saudi Arabia Univ Lille Nord France F-59000 Lille France UVHC TEMPO Lab PSI Team F-59313 Valenciennes France UVHC LAMIH Lab DIM Team F-59313 Valenciennes France

ISBN: (纸本)9782960053241

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partiel FJSP and Total FJSP, are tested. Computational results proved that the PSO developed is enough effective and efficient to solve flexible job-shop scheduling problem.

关键词： flexible job-shop scheduling problem makespan particle swarm optimization algorithm routing and scheduling

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Data Classification Based on the Hybrid Versions of the particle swarm optimization algorithm 7

Data Classification Based on the Hybrid Versions of the Part...

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7th Mediterranean Conference on Embedded Computing (MECO)

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

ISBN: (纸本)9781538656839

The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.

关键词： classification particle swarm optimization algorithm grid search algorithm SVM classifier radial basis kernel function

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Convergence Analysis of Self-adaptive Immune particle swarm optimization algorithm 15th

Convergence Analysis of Self-adaptive Immune Particle Swarm ...

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15th International Symposium on Neural Networks (ISNN)

作者： Jiang, Jingqing Song, Chuyi Ping, Huan Zhang, Chenggang Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028043 Peoples R China Inner Mongolia Univ Nationalities Coll Math Tongliao 028043 Peoples R China

ISBN: (纸本)9783319925370;9783319925363

The self-adaptive immune particle swarm optimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved better result compared with the classical particle swarm optimization algorithm. However, the theoretical support of the algorithm is equally important as the implementation of the algorithm. Therefore, this paper mainly uses the convergence theorem of random search algorithm and the mathematical induction to prove the convergence of SAIPSO algorithm, which will help the improvement and application of the algorithm in the future.

关键词： Immune algorithm particle swarm optimization algorithm Self-adaptation Convergence

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