派博傳思國際中心

標(biāo)題: Titlebook: Advances in Swarm Intelligence; 4th International Co Ying Tan,Yuhui Shi,Hongwei Mo Conference proceedings 2013 Springer-Verlag Berlin Heide [打印本頁]

作者: lexicographer 時間: 2025-3-21 20:06
書目名稱Advances in Swarm Intelligence影響因子(影響力)

書目名稱Advances in Swarm Intelligence影響因子(影響力)學(xué)科排名

書目名稱Advances in Swarm Intelligence網(wǎng)絡(luò)公開度

書目名稱Advances in Swarm Intelligence網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Advances in Swarm Intelligence被引頻次

書目名稱Advances in Swarm Intelligence被引頻次學(xué)科排名

書目名稱Advances in Swarm Intelligence年度引用

書目名稱Advances in Swarm Intelligence年度引用學(xué)科排名

書目名稱Advances in Swarm Intelligence讀者反饋

書目名稱Advances in Swarm Intelligence讀者反饋學(xué)科排名

作者: saphenous-vein 時間: 2025-3-21 20:35

作者: deficiency 時間: 2025-3-22 01:05

作者: Limerick 時間: 2025-3-22 06:58

作者: 時代 時間: 2025-3-22 12:06
Conception optimale de structures data, and then the particle swarm optimization algorithm is applied for piecewise area division and parameter optimization of the model. Simulation result shows that compared with traditional inversion method, better practicability and the higher significant wave height inversion precision are obtained by the proposed method.

作者: 津貼 時間: 2025-3-22 16:37
,Introduction à l’optimisation de formes,e, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.

作者: Indolent 時間: 2025-3-22 17:22

作者: 小淡水魚 時間: 2025-3-23 00:28
Cask Theory Based Parameter Optimization for Particle Swarm Optimizationt can be used to search the tuned parameters such as inertia weight ., acceleration coefficients c. and c., and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

作者: Scleroderma 時間: 2025-3-23 03:54
A Piecewise Linearization Method of Significant Wave Height Based on Particle Swarm Optimization data, and then the particle swarm optimization algorithm is applied for piecewise area division and parameter optimization of the model. Simulation result shows that compared with traditional inversion method, better practicability and the higher significant wave height inversion precision are obtained by the proposed method.

作者: Gobble 時間: 2025-3-23 07:59
Parameter Identification of RVM Runoff Forecasting Model Based on Improved Particle Swarm Optimizatie, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.

作者: 反對 時間: 2025-3-23 11:55

作者: 保存 時間: 2025-3-23 17:36

作者: PRISE 時間: 2025-3-23 18:49
Edurne Pozueta,Fermín M. Gonzálezhe ES based method is capable of driving robots to the purposed states generated by mechanical PSO without the necessity of robot localization. By this way, the whole robot swarm approaches the searched target cooperatively. This pilot study is verified by numerical experiments in which different robot sensors are mimicked.

作者: vascular 時間: 2025-3-24 01:56
Bénédicte Vanblaere,Geert Devose center position of its own swarm. Experiments are conducted on five test functions to compare with some variants of the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.

作者: 評論性 時間: 2025-3-24 06:19

作者: 天氣 時間: 2025-3-24 06:49

作者: 發(fā)酵劑 時間: 2025-3-24 11:33
,Introduction à l’optimisation de formes,T works as like as traditional motivator used in power system. By SIMULINK-MATLAB we implement the complete mathematical model of the system. The simulation results demonstrate that the Optimized Fuzzy Logic Control (OFLC) gets a better parameters of fuzzy sets using PSO, and realizes a good dynamic behavior compared with conventional FLC.

作者: 到婚嫁年齡 時間: 2025-3-24 17:50

作者: Lamina 時間: 2025-3-24 19:35
https://doi.org/10.1007/978-3-030-69345-9orithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm achieves better solutions and faster convergence.

作者: Mettle 時間: 2025-3-24 23:13

作者: CLOUT 時間: 2025-3-25 06:30
https://doi.org/10.1007/978-3-540-36856-4timization (PSO) algorithm, where the choice of the parameters is inspired by [4], in order to avoid diverging trajectories of the particles, and help the exploration of the feasible set. Moreover, we extend the ideas in [4] and propose a specific set of initial particles position for the bound constrained problem.

作者: 碎石 時間: 2025-3-25 08:46
Opposition-Based Learning Fully Informed Particle Swarm Optimizer without Velocityorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm achieves better solutions and faster convergence.

作者: 博愛家 時間: 2025-3-25 15:26
GSO: An Improved PSO Based on Geese Flight Theoryity. Moreover, the rules and hypotheses for formation flight adhere to all five basic principles of swarm intelligence. Therefore, the proposed geese-flight theory is highly rational and has important theoretical innovations, and GSO algorithm can be utilized in a wide range of applications.

作者: Torrid 時間: 2025-3-25 19:19

作者: Picks-Disease 時間: 2025-3-25 20:40
Maturity of the Particle Swarm as a Metric for Measuring the Collective Intelligence of the Swarmecause of the lack of the system’s awareness, and that a solution would be some adaptation of particle’s behavioural rules so that the particle could adjust its velocity using control parameters whose value would be derived from inside of the swarm system, without tuning.

作者: Obsequious 時間: 2025-3-26 01:59

作者: 偶像 時間: 2025-3-26 08:08
Interactive Robotic Fish for the Analysis of Swarm Behavioran execute certain behaviors integrating feedback from the swarm’s position, orientation and velocity. Here, we describe implementation details of our hardware and software and show first results of the analysis of behavioral experiments.

作者: Muscularis 時間: 2025-3-26 09:59
Particle Swarm Optimization in Regression Analysis: A Case Studyto obtain the minimum sum of absolute difference values between observed data points and calculated data points by the regression function. Experimental results show that particle swarm optimization can obtain good performance on regression analysis problems.

作者: nocturia 時間: 2025-3-26 16:18
Mechanical PSO Aided by Extremum Seeking for Swarm Robots Cooperative Searchhe ES based method is capable of driving robots to the purposed states generated by mechanical PSO without the necessity of robot localization. By this way, the whole robot swarm approaches the searched target cooperatively. This pilot study is verified by numerical experiments in which different robot sensors are mimicked.

作者: Neolithic 時間: 2025-3-26 20:24
Multi-swarm Particle Swarm Optimization with a Center Learning Strategye center position of its own swarm. Experiments are conducted on five test functions to compare with some variants of the PSO. Comparative results on five benchmark functions demonstrate that MPSOCL achieves better performances in both the optimum achieved and convergence performance than other algorithms generally.

作者: 勤勞 時間: 2025-3-26 22:44

作者: 熱情贊揚(yáng) 時間: 2025-3-27 02:11
Local and Global Search Based PSO Algorithm experience, which is the same as the standard PSO. Simulation results and comparisons with the standard PSO 2007 demonstrate that the proposed algorithm effectively enhances searching efficiency and improves the quality of searching.

作者: 財主 時間: 2025-3-27 06:23

作者: 小樣他閑聊 時間: 2025-3-27 13:02

作者: 細(xì)絲 時間: 2025-3-27 17:34
https://doi.org/10.1007/978-3-658-03097-1rithms have been simulated and compared with the particle swarm optimization and the simple particle swarm optimization. The simulations show that the algorithms have a higher convergence precision for some functions or a particular issue.

作者: BRAVE 時間: 2025-3-27 21:02
https://doi.org/10.1007/978-3-642-38703-6applications; design; graphs; machine learning; optimization algorithms; algorithm analysis and problem c

作者: 盟軍 時間: 2025-3-27 23:37

作者: Kindle 時間: 2025-3-28 03:29
Joachim Hereth Correia,Tim B. Kaiser enables us to examine collective behaviors in fish shoals. The system uses small wheeled robots, moving under a water tank. The robots are coupled to a fish replica inside the tank using neodymium magnets. The position of the robots and each fish in the swarm is tracked by two cameras. The robots c

作者: 粗鄙的人 時間: 2025-3-28 07:58

作者: headway 時間: 2025-3-28 11:20

作者: heart-murmur 時間: 2025-3-28 16:05
Karoline Afamasaga-Fuata’i,Greg McPhanom the bio-chemistry level. In this article, an artificial chemistry system which strikes a balance among closeness to reality, fast simulation speed and high flexibility is proposed. Preliminary results have shown that the model can simulate a general reversible reaction well.

作者: Omnipotent 時間: 2025-3-28 19:02
Karoline Afamasaga-Fuata’i,Greg McPhanima. In this article, a research work is introduced in?which the cooperative . strategies are analysed and the collective intelligence of the particle swarm is assessed according to the proposed .. The model is derived from the . (.) operational space and the model of .. The aim was to gain a more t

作者: FLACK 時間: 2025-3-29 00:55
Joseph D. Novak,Alberto J. Ca?as function is a nonlinear, constrained, and difficult problem which is solved by traditionally mathematical regression method. The regression process is formulated as a continuous, constrained, single objective problem, and each dimension is dependent in solution space. The object of optimization is

作者: ANA 時間: 2025-3-29 04:31

作者: 獨(dú)裁政府 時間: 2025-3-29 07:30

作者: 注視 時間: 2025-3-29 11:51
https://doi.org/10.1007/978-3-030-69345-9ptimiser without velocity is proposed for optimization problems. Different from the standard PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning in the algorithm. Besides, all personal best positions are considered t

作者: GLIB 時間: 2025-3-29 18:12

作者: Tincture 時間: 2025-3-29 23:41

作者: CLAP 時間: 2025-3-30 03:23
https://doi.org/10.1007/978-3-658-03097-1 computational efficiency was exceptional, the sum of errors was high. In order to demonstrate that the errors came from GPS readings instead of photography mistakes or erroneous computer codes, the authors designed and implemented an experiment and used it to verify the applicability of the PSO met

作者: 傻瓜 時間: 2025-3-30 07:59
https://doi.org/10.1007/978-3-540-36856-4vatives are unavailable and the use of exact derivative-free algorithms may imply a too large computational burden. There is plenty of real applications, e.g. several design optimization problems [1,2], belonging to the latter class, where the objective function must be treated as a ‘black-box’ and

作者: insurgent 時間: 2025-3-30 09:14
Conception optimale de structuresPSO). PSO is influenced by a high number of random values in order to simulate a more nature like behaviour. Based on these random numbers the optimization process may vary. Usually the uniform distribution is chosen but regarding certain underlying fitness functions this may not the best choice. To

作者: Arroyo 時間: 2025-3-30 13:55