摘要
针对联合火力打击任务规划中对兵力、火力与目标动态分配优化困难的问题,提出一种基于粒子群算法的联合火力打击任务规划智能优化算法。该算法以粒子群算法为基础,模拟鸟群的觅食行为设计智能优化算法,并在标准算法基础上引入遗传算法中的生物优胜劣汰机制,提升算法的迭代效率和全局寻优精度,根据联合火力打击任务规划的内在制约条件设计了衡量任务规划各方面综合性能的评估指标模型,并通过熵权法和理想点法获取联合火力打击任务规划综合评分。仿真结果表明:进化粒子群算法较标准粒子群算法和遗传算法具有更优越的迭代收敛效率和全局寻优能力,具备解决联合火力打击任务规划智能优化问题的能力。
Aiming at the difficulty in optimizing the dynamic allocation of strength,firepower and target in joint firepower mis⁃sion planning,this paper proposes an intelligent optimization algorithm for joint firepower mission planning based on particle swarm optimization.Based on the particle swarm optimization algorithm,the modified algorithm simulates the intelligent optimization algo⁃rithm of foraging behavior design of the bird group,and introduces the biological survival and the fittest mechanism in the genetic al⁃gorithm based on the standard algorithm to improve the iterative efficiency and global optimization precision of the algorithm.Accord⁃ing to the joint firepower the internal constraints of combat mission planning designs an evaluation index model that measures the comprehensive performance of all aspects of mission planning,and obtains the comprehensive score of joint firepower mission plan⁃ning through entropy weight method and ideal point method.The simulation results show that the evolutionary particle swarm optimi⁃zation algorithm has better iterative convergence efficiency and global optimization ability than the standard particle swarm optimiza⁃tion algorithm,and has the ability to solve the intelligent optimization problem of joint firepower mission planning.
作者
刘昊
宋敬峰
陈超
LIU Hao;SONG Jingfeng;CHEN Chao(Joint Operations College,National Defense University,Shijiazhuang 050000;Staff of No.31696 Troops of PLA,Jinzhou 121000;Staff of No.93123 Troops of PLA,Liaoyang 111000)
出处
《舰船电子工程》
2020年第4期21-26,47,共7页
Ship Electronic Engineering
关键词
进化粒子群算法
联合作战
火力打击任务规划
遗传算法
熵权法
理想点法
evolutionary particle swarm optimization
joint operations
firepower mission planning
genetic algorithm
entro⁃py weight method
ideal point method
