Journal of Xidian University ›› 2021, Vol. 48 ›› Issue (5): 239-248.doi: 10.19665/j.issn1001-2400.2021.05.027

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Improved NSGA-II algorithm and research on monitoring antenna optimization deployment

DU Wenzhan1(),YU Zhiyong1(),YANG Jian2(),JIANG Haibin1()   

  1. 1. Rocket Force University of Engineering 302 laboratory,Xi’an,710025,China
    2. Rocket Force University of Engineering 205 laboratory,Xi’an,710025,China
  • Received:2020-06-24 Online:2021-10-20 Published:2021-11-09


In order to solve the optimum deployment location of the ground radiation monitoring antenna to achieve accurate monitoring and efficient deployment,a multi-objective optimization model is constructed to maximize the coverage rate,minimize the redundancy rate and the number of uncovered obstacles.And the model is constrained by communication between antennas.By comparing the relationship between the distance between two obstacles and the maximum radius of the antenna's working coverge range,the reference points are preset as the antenna deployment location.The effects of the number of obstacles and the number of reference points on the deployment efficiency such as coverage rate and redundancy rate are compared by simulation.It is verified that the number of obstacles has a greater effect on the redundancy rate,and that the number of obstacles in the antenna coverage area is reduced at the expense of the increased redundancy rate.The second generation of the non-dominant sorting genetic (NSGA-II) algorithm is improved by using the memory to store the previous generations of non-dominant solutions.After changing the environment,the new optimal solution can be obtained faster and the convergence speed can be improved.The simulation results of the model and the improved algorithm show that the iteration speed is increased by more than 30% on average,and that the average iteration time is reduced by more than 25%,which verifies the effectiveness of the improved algorithm in response to dynamic changes.The convergence and diversity of the improved algorithm are verified by the algorithm evaluation index mIGDB.

Key words: multi-objective optimization, sensors deployment, improved NSGA-II algorithm, preset reference point, electromagnetic radiation source monitoring

CLC Number: 

  • TN92