Study of a particle swarm optimization method with multidirectional competition for the ground target attacking weapon-target allocation problem

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and proposed a particle swarm optimization method with multidirectional competition. The method uses an affinity propagation (AP) clustering algorithm to detect the location of the local optimal regions in the search space, introduces a multidirectional competition factor in the speed update of the particle swarm optimization (PSO) algorithm to guide the particles to compete and search in multiple directions to avoid the algorithm falling into a local optimum, thus accelerating the optimization. The simulation results show that the proposed method can greatly improve the solving efficiency of the GTA-WTA problem, and the obtained allocation scheme is also superior, and the larger the problem scale, the more obvious the effect.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.