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Titlebook: Advances in Swarm Intelligence; 5th International Co Ying Tan,Yuhui Shi,Carlos A. Coello Coello Conference proceedings 2014 Springer Intern

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樓主: 召喚
21#
發(fā)表于 2025-3-25 03:43:07 | 只看該作者
22#
發(fā)表于 2025-3-25 09:20:09 | 只看該作者
Capacity and Power Optimization for Collaborative Beamforming with Two Relay Clustersonly one though a series of mathematical manipulation. Then apply genetic algorithm (GA) to obtain the optimal weight value of the nonconvex problems. Simulation results show that our proposed approaches significantly outperform the previous methods conducted.
23#
發(fā)表于 2025-3-25 14:59:13 | 只看該作者
Renin, sodium and hypertension,lutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×70 in most data sets, furthermore, the WebDocs benchmark is handled with less than forty hours.
24#
發(fā)表于 2025-3-25 18:28:45 | 只看該作者
25#
發(fā)表于 2025-3-25 20:01:05 | 只看該作者
26#
發(fā)表于 2025-3-26 01:55:14 | 只看該作者
27#
發(fā)表于 2025-3-26 05:36:17 | 只看該作者
Symmetries and Effective Vertices,ance and solution quality. The results may verify the effectiveness and promising application of the proposed method in solving the ED problem when we are considering both controllable and uncontrollable DG in power system.
28#
發(fā)表于 2025-3-26 10:36:42 | 只看該作者
G. S. Singhal,G. Renger,Govindjeeions results are compared with the results obtained using standard PBIL and another diversity increasing PBIL called herein as PBIL with Adapting learning rate (APBIL). It is shown that Parallel PBIL approach performs better than the standard PBIL and is as effective as APBIL.
29#
發(fā)表于 2025-3-26 15:10:14 | 只看該作者
G. S. Singhal,G. Renger,Govindjee time detect the anomaly of hydropower unit vibration parameters. The results show that this model can effectively evaluate the performance of unit vibration, can more accurately detect the abnormal of unit vibration.
30#
發(fā)表于 2025-3-26 19:41:38 | 只看該作者
Comparison of Multi-population PBIL and Adaptive Learning Rate PBIL in Designing Power System Controions results are compared with the results obtained using standard PBIL and another diversity increasing PBIL called herein as PBIL with Adapting learning rate (APBIL). It is shown that Parallel PBIL approach performs better than the standard PBIL and is as effective as APBIL.
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