Total system cost minimization in CDMA PCS network using hierarchical genetic algorithms

2002 ◽  
Author(s):  
T.M. Chan ◽  
S. Kwong ◽  
K.F. Man ◽  
K.S. Tang
Keyword(s):  
Genetic Algorithms ◽  
Cost Minimization ◽  
Total System ◽  
System Cost

Optimizing total system cost by balancing unit cost against real cost

2007 ◽  
Vol 2007 (5) ◽  
pp. 11-13 ◽  
Author(s):  
Johan François
Keyword(s):  
Unit Cost ◽  
Total System ◽  
System Cost ◽  
Real Cost

scholarly journals System Cost Minimization in Cloud RAN With Limited Fronthaul Capacity

2017 ◽  
Vol 16 (5) ◽  
pp. 3371-3384 ◽  
Author(s):  
Jianhua Tang ◽  
Wee Peng Tay ◽  
Tony Q. S. Quek ◽  
Ben Liang
Keyword(s):  
Cost Minimization ◽  
System Cost ◽  
Cloud Ran

scholarly journals Statistical modeling and optimal energy distribution of cogeneration units by genetic algorithms

2022 ◽  
Vol 12 (2) ◽  
pp. 1131
Author(s):  
Othmane Maakoul ◽  
Hamid El Omari ◽  
Aziza Abid
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Energy Management ◽  
Cost Minimization ◽  
Operating Cost ◽  
Optimization Criterion ◽  
Efficiency Improvement ◽  
Fuel Cost ◽  
Gas Emissions ◽  
Simulation Results

Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.

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