scholarly journals Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar

2021 ◽  
Author(s):  
Xiaopeng Yang ◽  
Yuqing Li ◽  
Feifeng Liu ◽  
Tian Lan ◽  
Long Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Differential Operator ◽  
Optimization Method ◽  
Adaptive Genetic Algorithm ◽  
Antenna Position ◽  
Aperture Radar

scholarly journals Optimal Operation Model of Drainage Works for Minimizing Waterlogging Loss in Paddy Fields

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2811
Author(s):  
Zhenyang Liu ◽  
Yujiang Xiong ◽  
Juzeng Xu ◽  
Shihong Yang ◽  
Zewei Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Global Climate ◽  
Operation Time ◽  
Extreme Rainfall ◽  
River System ◽  
Optimization Method ◽  
Optimal Operation ◽  
Irrigation District ◽  
Adaptive Genetic Algorithm ◽  
Extreme Rainfall Events

The risk of flood or waterlogging in irrigation districts has increased due to global climate change and intensive human activities. A Model of Optimal Operation of Drainage Works (MOODW) for flat irrigation district was established by incorporating the hydrological model of waterlogging process and waterlogging loss estimation, which was solved by an optimization method of genetic algorithm. The model of waterlogging process was built based on a modified Tank model and hydrodynamic model for the ditch-river system. The waterlogging loss is calculated under the condition of inconstant inundated depth by linear interpolation. The adaptive genetic algorithm with the global optimization function was selected to solve the model. With an extreme rainfall events in Gaoyou irrigation district as cases, results showed that operation time and numbers of pumps increased; thus, operating costs were 1.4 times higher than before, but the yield loss of rice decreased by 35.4% observably. Finally, the total waterlogging loss was reduced by 33.8% compared with the traditional operation of waterlogging work. The most significant improvement was found in units with high waterlogging vulnerability. The MOODW can provide the waterlogging information visually and assist the district manager in making a reasonable decision.

scholarly journals Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 687
Author(s):  
Fang Ye ◽  
Jie Chen ◽  
Yuan Tian ◽  
Tao Jiang
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Task Assignment ◽  
Optimization Method ◽  
Adaptive Genetic Algorithm ◽  
Mutation Operators ◽  
Fitness Value ◽  
Coupling Constraints ◽  
Crossover And Mutation ◽  
And Task

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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