基于改进粒子群算法的反舰导弹航路规划研究  

Research on Missile Route Planning Based on Improved Particle Swarm Optimization Algorithm

作　　者：孔姝睿 刘淑芬[2] KONG Shu-ruil;LIU Shu-fen(College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454150, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China)

机构地区：[1]河南理工大学计算机科学与技术学院,河南焦作454150 [2]吉林大学计算机科学与技术学院,吉林长春130012

出　　处：《测控技术》2017年第11期66-69,共4页Measurement & Control Technology

基　　金：国家自然科学基金项目(61472160);国家科技支撑计划项目(2014BAH29F03)

摘　　要：针对飞行器航路规划问题,提出了一种改进粒子群算法。在标准粒子群算法的基础上,对惯性权重系数进行了非线性的调整,对学习因子进行线性和非线性的优化,并引入遗传算法中的交叉算子,将较好粒子与较差粒子进行交叉,保证了种群的多样性,从而提高算法的全局搜索能力。为了验证算法的可行性与有效性,对其进行仿真测试。实验结果表明,与标准粒子群算法、线性惯性权重相比,改进的粒子群算法表现出较强的全局搜索能力和较好的收敛性。According to the craft route planning, a kind of improved particle swarm optimization（PSO） algo- rithm is proposed. On the basis of the standard PSO algorithm, the inertia weight coefficient is adjusted linear- ly, linear and nonlinear optimization of the learning factor is realized, and the crossover operator of genetic algo- rithm is introduced to make the better particles cross with the worse particles to ensure the diversity of popula- tion and improve the global search ability of the algorithm. In order to verify the feasibility and effectiveness of the algorithm, a simulation test is carried out. The experimental results show that the improved PSO algorithm has a strong global search ability and a better astringency compared with the standard PSO algorithm and the linear inertia weight.

关 键 词：粒子群算法 航路规划 惯性权重 学习因子 交叉粒子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]