Abstract
This paper develops an improved non dominated sorting genetic algorithm II (NSGA-II) based on objective importance vector γ, abbreviated as γ-NSGA-II. Different importance levels for the multiple objectives are incorporated in the objective importance vector, which is applied to determine the individual selection of sorting individuals in the critical layer. And such an individual selection strategy is developed to the NSGA-II algorithm in order to obtain the optimized solution for a task which has multiple objectives with different importance. The differences between the γ-NSGA-II algorithm and the traditional NSGA-II algorithm are discussed in detail. A notch filter is designed for the conducted emission suppression of a transformer rectifier unit (TRU) used in C919 flight testing, and then the parameters optimization design of a notch filter is discussed and conducted based on the γ-NSGA-II algorithm. The non-linear relationship between the filter's parameters and the suppression effect of the conducted emission is also discussed with the help of an electromagnetic compatibility (EMC) evaluation model based on a back propagation (BP) neural network. The experimental results show that the optimized design of the notch filter is effective and the improved γ-NSGA-II algorithm be more specific.
Highlights
In recent years, intelligent algorithms have been gradually applied to many new fields, including fault diagnosis, system control, and parameter optimization. [1]–[6]
This paper develops an improved Non dominated sorting genetic algorithm II (NSGA-II) algorithm based on objectives importance vector γ and an an individual selection strategy of dimension reduction sorting for individuals in the critical layer
As a multi-objective optimization algorithm, γ -non dominated sorting genetic algorithm (NSGA)-II introduces an objectives importance vector γ based on the NSGA-II algorithm, reduces the dimension of the γ according to the importance of each objective and carries out fast non-dominant sorting for individuals in the critical layer
Summary
Intelligent algorithms have been gradually applied to many new fields, including fault diagnosis, system control, and parameter optimization. [1]–[6]. This paper develops an improved NSGA-II algorithm based on objectives importance vector γ and an an individual selection strategy of dimension reduction sorting for individuals in the critical layer. As a multi-objective optimization algorithm, γ -NSGA-II introduces an objectives importance vector γ based on the NSGA-II algorithm, reduces the dimension of the γ according to the importance of each objective and carries out fast non-dominant sorting for individuals in the critical layer. Hk is more than (N -L), the least important optimization objectives from M are removed and the individuals of Hk by fast are layered by a non dominated sorting approach These steps are repeated until the total number of individuals in Pt+1 is equal to N. In order to suppress the conducted emissions and make them meet the standard requirements, it is necessary to design a notch filter to filter the interference level in this frequency band and transmit the 400 Hz power to the load without attenuation
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