基于直觉模糊遗传的武器—目标分配问题优化

Optimization of weapon-target assignment problem by intuitionistic fuzzy genetic algorithm

针对求解武器—目标分配(weapon-target assignment,WTA)问题的传统算法容易早熟和收敛较慢的缺点,提出一种直觉模糊遗传算法,采用模拟退火的Meta-Lamarckian学习策略和自适应变异,提高了求解WTA问题的效益和速度。首先考虑了WTA问题的各种约束条件,以剩余目标威胁最小和攻击伤害值最大为目标,建立了数学模型,定义了目标函数和约束函数的隶属度和非隶属度函数,通过"最小—最大"算子构建了直觉模糊WTA问题模型,并设计了模拟退火的Meta-Lamarckian学习策略和自适应变异,增强算法的局部寻优能力和后期收敛速度。通过算例仿真并与GA、PSO等算法比较分析,验证了该方法的有效性。

Aimed at the shortcoming of precocity and slow convergence in the application of traditional algorithms to solve the weapon-target assignment(WTA)problem,this paper proposed an intuitionistic fuzzy genetic algorithm that combined with simulated annealing Meta-Lamarckian learning strategy and adaptive mutation to improve the efficiency and speed of solving WTA problem.Firstly,it considered the various constraint functions of WTA problem,in which made the threat of remaining targets minimum and the damage from attacks maximum,established the mathematical model.Next,it defined the membership and nonmembership functions of object and constraint function,and built the intuitionistic fuzzy WTA model on the basis of the“min-max”operator.Then,it designed a strategy of Meta-Lamarckian learning for simulated annealing and adaptive mutation to enhance the capability of local search and the speed of upper convergence for the algorithm.Finally,this method is effective via the simulation and the analysis of comparison with GA,PSO.