Energy Optimization of RFID Networks using Genetic Algorithm

Author(s):  
Nazish Irfan ◽  
Mustapha C.E. Yagoub ◽  
Khelifa Hettak
2014 ◽  
Vol 672-674 ◽  
pp. 1358-1363
Author(s):  
Liu Shu ◽  
Fang Liu ◽  
Xiu Yang

Accessing electric vehicle (EV) into micro-grid (MG) by battery-swapping station (BSS) will not only reduce the negative impact brought by EVs which are directly accessed into MG, but also improve the capacity of MG to absorb more renewable energy. That BSS is regarded as schedulable load is guided to avoid peak and fill valley according to TOU. As a result, the gap between peak and valley of MG is decreased and the operation efficiency of MG is elevated. A specific MG is taken as the studying object and the minimum operating cost is regarded as the optimizing goal, then the genetic algorithm is used to optimize the outputting of each micro-source and the charging power of BSS so that the optimal operation is realized.


Author(s):  
Takemasa Arakawa ◽  
◽  
Toshio Fukuda ◽  
Naoyuki Kubota ◽  

In this paper, we apply a virus-evolutionary genetic algorithm with subpopulations (VEGAS) to a trajectory generation problem for redundant manipulators through energy optimization. VEGAS is based on the virus theory of evolution and VEGAS has some subpopulations that usually evolve independently. In the same subpopulation, a virus infects host individuals. And a virus sometimes immigrates from one subpopulation to another. The genetic information from one subpopulation propagates in another subpopulation only through immigration of the virus. The energy-optimized collision-free trajectory was found successfully using VEGAS.


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