基于混合粒子群算法的火力分配模型
A Firepower Allocation Model Based on Hybrid Particle Swarm Optimization Algorithm
林剑 1赵军岩1
作者信息
- 1. 陆军特种作战学院 广西 桂林 541002
- 折叠
摘要
火力作战是现代化作战的基本形式,是提升我军战场作战能力的一项关键研究.针对传统粒子群算法适用于连续解空间及容易陷入局部极值的问题,提出将粒子群算法和遗传算法融合的混合粒子群算法,改造交叉算子和变异算子,充分利用个体当前极值和粒子群整体极值信息,并结合模拟退火的思想,以一定的概率接受交叉变异后较差的粒子.算法既保留粒子群算法快速的局部搜索优势,又具备遗传算法的全局优化性能.仿真结果表明,混合粒子群算法在多平台多武器防空反导的火力分配中具有比遗传算法更优的性能.
Abstract
Firepower combat is a fundamental form of modern warfare and a key research area for enhancing the bat-tlefield operational capabilities of the Chinese military.In response to the traditional particle swarm optimization algorithm's applicability to continuous solution spaces and its tendency to fall into local optima,this paper proposes a hybrid particle swarm optimization algorithm that integrates the genetic algorithm.The crossover and mutation opera-tors are modified to fully utilize both the individual's current optimal values and the overall optimal values of the parti-cle swarm.In addition,the proposed algorithm incorporates the concept of simulated annealing and accepts inferior particles after crossover and mutation with a certain probability.It retains the rapid local search advantage of the par-ticle swarm optimization algorithm while possessing the global optimization performance of the genetic algorithm.The simulation results show that the hybrid particle swarm optimization algorithm has superior performance in firepower al-location of multi-platform and multi-weapon air and missile defense systems compared to the genetic algorithm.
关键词
防空反导/多平台多武器火力分配/混合粒子群算法Key words
air and missile defense/multi-platform and multi-weapon firepower allocation/hybrid particle swarm optimization algorithm引用本文复制引用
出版年
2024
NETL
NSTL
万方数据