基于遗传模拟退火算法的空袭兵力分配及优化

对遗传模拟退火算法中的交叉、变异操作进行了改进,并实施了最优保留策略,形成了改进遗传模拟退火算法.以突击效果最大化和兵力损失最小化为目标函数,以空袭兵力总量的限制、空袭兵器挂载类型的限制等为约束条件,建立了空袭兵力分配及优化模型.在考虑兵力分配模型特点的基础上,利用改进遗传模拟退火算法求解.通过与多目标数学规划和标准遗传算法优化进行的比较表明,该方法能够有效地解决带约束的多目标优化问题.

Distributing and optimizing air-raid forces based on genetic simulated annealing algorithm

The crosser and mutation in the genetic simulated annealing algorithm have been improved.The optimized reserved strategy is used to form the improved genetic simulated annealing algorithm.With the largest destroying effectiveness and the least loss as the object function,and the limitation of the total number of the air-raid forces and the types of weapons as the restrictive terms,the air-raid forces distributing and optimizing model are established.Based on the features of air-raid forces distribution, the improved genetic simulated annealing algorithm is used in network reconfiguration.By comparing with multi-objective math programming and standard GA,the results show that this me-(thod) can solve the problem of constrained multi-objective optimization.