应用于武器-目标分配问题的量子行为粒子群优化算法

摘要

WTA problem is vital in modern warfare. The WTA model is built aiming at minimum failure probability in allocating weapons for shooting all the targets. This paper puts forward a quantum behaviour particle swarm optimisation algorithm with inertia weight adaptive adjustment to overcome the deficiencies of premature convergence and low optimisation efficiency the existing algorithm has in solving such kind of problems. First, the concept of focusing distance changing rate is introduced, the inertial weight factor is formulated as the function of focusing distance rate so as to provide the algorithm with dynamic adaptability. Meanwhile, an effective method of judging and preventing premature and stagnation is embedded into the algorithm. The optimisation example shows that this algorithm can effectively solve the WTA problems.%武器一目标分配(WTA)问题是现代战争中一个十分重要的问题.以分配武器迎击全部目标的失败概率最小为目标,构建武器一目标分配问题模型；针对已有算法求解这类问题存在的早熟收敛、优化效率较低的缺点,提出一种惯性权重自适应调整的量子行为粒子群优化算法.首先引入聚焦距离变化率的概念,将惯性权重因子表示为关于聚焦距离变化率的函数,从而使算法具有动态自适应性；同时在算法中嵌入一种判断和避免搜索早熟和停滞的有效方法.优化实例的结果分析表明,该算法能有效地解决武器-目标分配问题.