Multi-interceptor Target Allocation Based on Improved Particle Swarm Optimization Algorithm

Firstly, aiming at the tactical relationship between multiple interceptors and targets, this paper proposes an indicator function and establishes a target allocation model for multiple interceptors. Then it designs an improved hybrid particle swarm optimization algorithm. This algorithm uses the linear decreasing weight method to solve the contradiction between global search ability and convergence precision. Hybrid operators in genetic algorithms are introduced to avoid local optimal solutions. And combined with the idea of tabu search, the algorithm reduce the round-trip search of the original algorithm. The simulation results show that the designed algorithm can give a better solution to the target allocation problem of multiple interceptors.