改进型布谷鸟搜索算法的防空火力优化分配模型求解

针对防空火力优化分配中火力资源易浪费且易延误战机的问题，将毁伤概率门限、飞临时间以及威胁度等因素相结合，构建一种改进的防空火力优化分配模型。基于此模型，提出一种多种群并行布谷鸟搜索算法求解防空火力多维整数优化分配问题。利用多个种群同时进行全局探索和局部开发，并通过移民算子进行各种群间的信息交流；为进一步提高全局探索能力，引入柯西变异算子构建新的全局搜索模型；在算法局部开发过程中，采用贪婪方式，逐维搜索。仿真结果表明：所建火力优化分配模型能有效地抓住战机，避免火力资源浪费；所提优化算法能较好地平衡全局探索和局部开发，在保证较高收敛速度的同时，提高了全局探索能力。

Improved Cuckoo Search Algorithm for Solving Antiaircraft Weapon-target Optimal Assignment Model

In antiaircraft weapon-target optimal assignment, the firepower resources are easy to waste and a combat opportunity could be missed. An air defense firepower improved optimal assignment model is constructed by combining damage probability threshold, flying time and threat degree. On this basis, a multi group parallel cuckoo algorithm (MPCSA) is proposed to solve the multi-dimensional optimization problem of air defense firepower. Multiple populations are used for global exploration and local development at the same time, and the migration operator is used to exchange information among different populations. In order to further improve the global search ability, Cauchy mutation operator is introduced to construct a new global search model. In the process of algorithm local development, the greedy method is applied to local development. The simulated results show that the weapon-target optimal assignment model can be used to effectively seize the opportunity for combat and avoid the waste of firepower resources. The proposed optimal algorithm can effectively balance the global exploration and local development, and the global exploration ability is improved while ensuring higher convergence speed.