基于优化人工蜂群算法的重力匹配导航方法  

Gravity Matching Navigation Method Based on Optimized Artificial Bee Colony Algorithm

作　　者：田军 赵博[2] 

机构地区：[1]武汉市武昌长虹桥37-1,武汉430064 [2]哈尔滨工业大学鞍山32业技术研究院,鞍山114000

出　　处：《计算机与数字工程》2015年第7期1193-1195,1206,共4页Computer & Digital Engineering

摘　　要：人工蜂群算法以其低复杂度和高精度等优点有望取代传统重力匹配方法中的非智能搜索策略,但是,引入人工蜂群算法的重力匹配策略仍会存在抗干扰性能差和可靠性低的问题。论文引入差分思想对人工蜂群的适应度函数进行优化,引入可调权值参数并确定一组最佳权值,最大程度减少惯导信息误差对厄特弗斯效应改正和正常重力计算的影响。仿真结果表明,优化后的重力匹配算法具有更高的匹配精度。Artificial bee colony algorithm is able to replace non intelligent search strategy for traditional gravity matc- hing methods to enhance the efficiency of gravity matching navigation for its fast convergence speed and low complexity. However, the problems of anti-interference performance and reliability for gravity matching strategy based on ABC still ex- ist. To reduce the influence of INS error and normal gravity calculation error, this article improves the fitness function with difference thought, in which adjustable parameters are introduced and the optimal values are confirmed. The simulation re- suits show that the optimized gravity matching algorithm can obtain higher matching accuracy.

关 键 词：人工蜂群算法 重力匹配 差分思想 参数优化 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]