摘要
在建立多种类型武器目标分配模型的基础上,提出了一种求解该模型的改进粒子群算法。首先,定义粒子聚焦距离变化率,使惯性权重依据聚焦距离变化率自适应调整;其次,采用速度最大值线性递减的策略平衡算法收敛精度与全局寻优能力之间的矛盾;最后,粒子替换策略使算法改善了因自适应惯性权重的引入而造成收敛速度变慢的问题。仿真结果表明,提出模型和算法合理有效,算法收敛快,适合求解各种种群规模的武器目标分配问题。
On the basis of establishing various types of weapon target assignment models,an improved particle swarm algorithm is proposed to solve the model. First of all,inertia weight is becoming adaptive expressed as functions of focus distance changing rate by defining them.Second,the strategy balancing algorithm of maximum speed linear regression is adopted to balance the contradiction between convergence accuracy and the global optimization ability; Finally,particle replacement strategy improves the algorithm the slow convergence speed problem caused by the introduction of self-adaptive inertia weight. Simulation results show that the proposed model and algorithm are reasonable and effective with fast converges,which are suitable for solving weapon target assignment problem of all kinds of population size.
出处
《火力与指挥控制》
CSCD
北大核心
2014年第12期58-61,共4页
Fire Control & Command Control
基金
国家自然科学基金(61004127)
山西省自然科学基金资助项目(2013011017-7)
