Weapon Target Allocation Based on GA-APSO Algorithm

Abstract

The Weapon Target Allocation (WTA) problem is a critical combinatorial optimization challenge in defense strategy, aimed at optimizing the allocation of limited weapons to various targets to maximize overall combat effectiveness. Given its NP-hard nature and the increasing complexity of modern battlefield environments, efficiently solving the WTA problem has become increasingly important. This paper proposes an improved Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) hybrid, referred to as the GA-APSO algorithm, to address the static WTA problem. By incorporating chaotic initialization techniques and adaptive parameter adjustment mechanisms, the algorithm enhances global search capability and computational efficiency. Experimental results demonstrate that the GA-APSO algorithm surpasses traditional PSO, GA, and their hybrid GA-PSO in terms of stability, optimization performance, and global solution accuracy. The first experiment (performance test) validates the effectiveness of the algorithm in solving the static WTA problem, while the second experiment evaluates its scalability and performance in large-scale static WTA scenarios, demonstrating that the algorithm provides a reliable solution for weapon target allocation in complex battlefield environments.

Keywords

• WTA
• PSO
• GA
• adaptive
• chaotic initialization