An adaptive genetic algorithm for parameter estimation of biological oscillator models to achieve target quantitative system response

2013 ◽  
Vol 13 (1) ◽  
pp. 119-127 ◽  
Author(s):  
Martin Stražar ◽  
Miha Mraz ◽  
Nikolaj Zimic ◽  
Miha Moškon
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
System Response ◽  
Adaptive Genetic Algorithm ◽  
Biological Oscillator ◽  
Oscillator Models

scholarly journals A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm

2016 ◽  
Vol 48 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Song Zhang ◽  
Ling Kang ◽  
Liwei Zhou ◽  
Xiaoming Guo
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Flood Routing ◽  
Adaptive Genetic Algorithm ◽  
Muskingum Model ◽  
Elite Strategy ◽  
Data Application ◽  
Different Characteristics

First, a novel nonlinear Muskingum flood routing model with a variable exponent parameter and simultaneously considering the lateral flow along the river reach (named VEP-NLMM-L) was developed in this research. Then, an improved real-coded adaptive genetic algorithm (RAGA) with elite strategy was applied for precise parameter estimation of the proposed model. The problem was formulated as a mathematical optimization procedure to minimize the sum of the squared deviations (SSQ) between the observed and the estimated outflows. Finally, the VEP-NLMM-L was validated on three watersheds with different characteristics (Case 1 to 3). Comparisons of the optimal results for the three case studies by traditional Muskingum models and the VEP-NLMM-L show that the modified Muskingum model can produce the most accurate fit to outflow data. Application results in Case 3 also indicate that the VEP-NLMM-L may be suitable for solving river flood routing problems in both model calibration and prediction stages.

Multi-Objective Fleet Assignment Problem Based on Self-Adaptive Genetic Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2895-2900 ◽  
Author(s):  
Xiao Yang ◽  
Bo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Assignment Problem ◽  
Network Capacity ◽  
Total Carbon ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Route Network ◽  
Assignment Model ◽  
Key Factor

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

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