摘要
针对舰载火力打击中的武器目标分配问题,设计了一种改善的混合粒子群优化算法来求解。对粒子更新速度的最大值进行线性递减,使得前期加强全局寻优能力,后阶段提高收敛能力;采用异步变化的学习因子,以及基于正切函数的惯性权重改进法来解决全局搜索能力与收敛精度之间的矛盾;引进了遗传算法中的杂交算子并采取模拟退火思想更新粒子,避免得到局部最优解。仿真结果显示,设计的算法能有效适宜地求解武器目标分配问题。
A hybrid improved particle swarm optimization(PSO)algorithm is designed to solve the problem of weapon target assignment in shipborne fire strike.Linear regression of maximum speed is used to enhance the global search capability in the early stage and improve the convergence ability at the later stage.The asynchronous change learning factor and the inertia weight improvement method based on tangent function are adopted to solve the contradiction between global search ability and convergence accuracy,and to balance global and local optimization ability.The hybridization of genetic algorithm and simulated annealing algorithm is introduced to update particles,so as to avoid falling into local optimum.Simulation results show that the designed algorithm can effectively and appropriately solve the weapon target assignment problem.
作者
陈曼
周凤星
CHEN Man;ZHOU Feng-xing(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《火力与指挥控制》
CSCD
北大核心
2018年第11期72-76,共5页
Fire Control & Command Control
基金
国家自然科学基金资助项目(61174106)
关键词
粒子群算法
异步
惯性权重改进
杂交
模拟退火
particle swarm optimization
asynchronous
inertia weight improvement
hybridization
simulated annealing
