多层防御模式下武器目标分配决策的群体智能优化算法

武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.

Swarm Intelligence Optimization Algorithms for Weapon Target Allocation Problem in Multilayer Defense Scenario

Weapon Target Allocation(WTA) is a classic NP-complete problem in the field of military operations research,to which the algorithms with polynomial time that can find accurate solution have not been established yet.In this paper,solving method for WTA problem in multilayer defense scenario under constraints of weapons availability,ground area availability at assets,cost budget for operation and maintenance,and manpower to operate weapons,was studied.Two kinds of swarm intelligence algorithms,i.e.,Particle Swarm Optimization(PSO) and Ant Colony Optimization(ACO),were applied to solve the WTA problem.The implementation schemes of PSO and ACO were described.The performance of these two algorithms was evaluated through a numerical example.The results show that, PSO and ACO are able to find approximate optimal solutions of high quality;hence,they can be taken as efficient algorithms to solve WTA problems.However,PSO is superior to ACO with respect to all three performance index,including solution quality,algorithmic robustness,and computational efficiency.