Weapon-Target Assignment Based on Improved PSO Algorithm

Abstract

In the research of modern combat command, the weapon-target assignment (WTA) problem contains many variables, which is a typical NP (Non-deterministic Polynomial) problem. The WTA optimal allocation model of multi-target cooperative attack is established by synthesizing various factors affecting operational effectiveness evaluation. Aiming at the shortcoming that the basic PSO algorithm is easy to fall into the local optimal solution, this paper proposes an improved PSO algorithm to improve the calculation speed and accuracy. The improved PSO algorithm uses the inertia weight of nonlinear decrement, improves the acceleration constant and introduces the adaptive mutation operation, which improves the convergence speed and accuracy of the algorithm, and avoids falling into the local optimal solution. Aiming at the WTA problem, the decimal coding strategy is adopted for the particle, and the position and velocity of the particle are discretized and limited by the boundary. Finally, the improved PSO algorithm is obtained. For large-scale WTA problems, the improved PSO algorithm is compared with the basic PSO algorithm. The simulation results show that the improved PSO algorithm speeds up the convergence speed of the algorithm, has better solution accuracy, and can effectively solve the WTA problem.