Particle Swarm Optimization Based on Genetic Operators for Sensor-Weapon-Target Assignment

Abstract

In the modern battlefields based on network, guided weapons highly rely on the sensors, so the benefit of assigning a given weapon to a target often depends on the pre-assigned sensor. in order to solve sensor-weapon-target (SWT) assignment which is an important activity involved in planning and executing a course of battle action, a model of SWT problem is established firstly. Secondly, particle swarm optimization based on genetic operators is put forward to solve the model, in which the restriction of problems is transformed by coding solutions, according to optimal solutions of population and individual, the new particle is updated by crossover, mutation and selection operators. Finally, after the numerical experiment of the algorithm, it is proved to be feasible and effective, especially in solving large-scale problems, it shows much better performance.