An Adaptive Optimization Algorithm for Antenna Deployment in Dynamic Environment

In this paper, an adaptive convergence method is investigated to optimize the prediction-based particle swarm optimization (PBPSO) algorithm that has been adopted for multistatic radar system surveilling multiple dynamic regions. We propose two parameters to measure the convergence and diversity. Unlike the traditional PBPSO method, which requires extensive experimentation to determine the number of iterations, the proposed method measures the Pareto Front's convergence degree and diversity to stop the iteration adaptively resulting in significantly better efficiency solving the optimal problem. Firstly, by calculating the parameter proposed in this paper, the algorithm perceives its degree of convergence and diversity. Then by comparing its own degree of convergence and diversity with a threshold set in accordance with the input requirements, the algorithm can determine whether to stop iteration adaptively. Numerical results show the proposed algorithm outperforms the traditional one in optimizing the antenna deployment scheme in a dynamic environment.