Genetic Algorithm Optimized Direct Torque Control of Mathematically Modeled Induction Motor Drive Using PI and Sliding Mode ControllerThis research introduces a genetic algorithm optimization control technique t
Agenetic algorithmis a metaheuristic algorithm based on Darwin's evolution of biological systems, which belongs to a larger class of evolutionary algorithms. It was developed by Holland in 1975[83]and implements the operators of crossover and recombination, mutation and selection mathematically. A gen...
For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The ...
As an example, if we assume that the forecasted daily gas availability and amount of oil extracted for a period if 30 days is provided to us (Fig. 9), a plot of them over the solutions of the MO problem will appear as Fig. 10. Although our algorithm cannot guarantee to achieve the ...
several issues need to be addressed when applying genetic algorithm (GA) to cooperative control in MAS. First, existing studies in this area often focus on simple agent dynamics, such as single or double integrators. The cooperative control of high-order nonlinear MAS needs to be investigated, ...
First, the algorithm assigns random values for each design variable. Then, it calculates the fitness function for each member. If the member does not satisfy any of the constraints, a large value (penalty value) is assigned to the fitness function of this infeasible member. The reproduction ...
One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations...
and robustness to model misspecification. Given the near impossibility of deriving these results mathematically, they must resort to simulation. The whole process is time consuming and error prone due to the abundance of data and the small expected effect sizes. A single misstep can lead to false...
One-generation simulation of a simple genetic algorithm. In this example, the best solution seems very easy to achieve, so an SGA seems unnecessary; however, in real-life applications of SGA, a population size can be as large as 100,000 and a chromosome can contains up to 10,000 genes....
Although this algorithm can handle different weights for different travel time components, the same time value for all travel time components was applied to this example to show the absolute time amount for each travel time component and show how the trade-off between travel time components works....