CAJ | 학술논문

利用协同多目标攻击战术的特定知识,并结合粒子群算法,提出了一种用于空战决策的启发式粒子群算法.该算法利用粒子群算法对解空间探索能力强,容易跳出局部最优陷井及启发式算法局部搜索能力强的优点,快速、高效地对全局最优值进行搜索.该算法通过求解友机导弹对目标的最优分配来确定空战决策方案.仿真实验结果表明,本文算法对最优空战决策方案的搜索性能明显优于普通粒子群算法及其他两种遗传算法.
이용협동다목표공격전술적특정지식,병결합입자군산법,제출료일충용우공전결책적계발식입자군산법.해산법이용입자군산법대해공간탐색능력강,용역도출국부최우함정급계발식산법국부수색능력강적우점,쾌속、고효지대전국최우치진행수색.해산법통과구해우궤도탄대목표적최우분배래학정공전결책방안.방진실험결과표명,본문산법대최우공전결책방안적수색성능명현우우보통입자군산법급기타량충유전산법.
Combining the heuristic algorithm (HA) developed based on the specific knowledge of the cooperative multiple target attack (CMTA) tactics and the particle swarm optimization (PSO), a heuristic particle swarm optimization (HPSO) algorithm is proposed to solve the decision-making (DM) problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts (FAs) to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA-based algorithms in searching for the best solution to the DM problem.