基于改进花朵授粉算法的防空部署优化研究

火力单元部署具有多目标、大规模和不确定性等特点,利用花朵授粉算法求解火力单元部署问题。针对花朵授粉算法存在易陷入局部最优、鲁棒性差等问题,为此提出小生境混沌花朵授粉算法。为增加算法搜索的广度,使用小生境技术保持种群的多样性,提升了算法的全局优化能力;引入逻辑自映射函数产生的混沌序列对精英个体进行局部优化,增强算法的搜索精度和收敛速度;将改进算法应用至火力单元优化部署模型中,仿真结果验证了改进算法比花朵授粉算法和其他基础优化算法能更合理高效地分配火力单元,实现武器作战效能最大化。

Air Defense Deployment Optimization Based on Improved Flower Pollination Algorithm

Firepower unit deployment has many characteristics such as multi-objective, large-scale and uncertainty, and flower pollination algorithm is used to solve the deployment of firepower unit.In order to solve the problems of flower pollination algorithm which is easy to fall into local optimum and poor robustness, this paper proposes a chaotic flower pollination algorithm for niche.In order to increase the breadth of algorithm search, niche technology is used to keep the diversity of the population and the global optimization ability of the algorithm is improved.The chaotic sequence generated by the logic self-mapping function is introduced to locally optimize the elites and enhance the search accuracy and convergence speed of the algorithm.The improved algorithm is applied to the optimization deployment model of firepower unit.The simulation results show that the improved algorithm can distribute the firepower unit more rationally and efficiently than the flower pollination algorithm and other basic optimization algorithms, and maximize the combat effectiveness of the weapon.