文献类型：期刊文献

中文题名：基于限域拟牛顿法的混沌类电磁学机制算法及其在路径寻优中的应用

英文题名：Application of chaotic electromagnetism mechanism algorithm based on limited memory Broyden-Fletcher-Goldfarb-Shanno in path planning

作者：乔现伟[1];乔蕾[1]

第一作者：乔现伟

机构：[1]河南财经政法大学数学与信息科学学院

第一机构：河南财经政法大学数学与信息科学学院

年份：2015

卷号：35

期号：3

起止页码：696-699

中文期刊名：计算机应用

外文期刊名：Journal of Computer Applications

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2015_2016】；

基金：国家自然科学基金资助项目(U1304102)

语种：中文

中文关键词：类电磁学算法;混沌映射;路径寻优;测试函数

外文关键词：Electromagnetism Mechanism （EM） ; chaotic mapping; path planning; test function

摘要：针对类电磁学(EM)算法后期"开采"能力不够、解精度不高且易陷入早熟的问题,提出了一种结合混沌映射和限域拟牛顿(L-BFGS)局部寻优算子的混沌类电磁学算法。其主要思想是在类电磁学算法后期采用限域拟牛顿算子取代类电磁学算法局部寻优算子进行局部搜索;在算法整个寻优过程加入混沌映射,利用混沌映射随机遍历的特性,生成新个体跳出局部从而保持种群多样性。通过对3个连续域测试函数的仿真比较,表明该算法后期能有效地跳出局部最优,较基本类电磁学算法在收敛速度方面有明显优势,较粒子群算法(PSO)和加速度系数随时间变化的粒子群算法(TVAC)在解的精度以及快速收敛方面更佳;通过在路径寻优中的应用结果对比表明该算法较元胞蚁群算法(ACO)、粒子群算法在路径寻优中能得到最佳路径,说明其在离散域问题中具有更好的适用性。
According to the problem of Electromagnetism Mechanism （EM） algorithm which may easily get into local optimal solution and has poor search capability, this paper combined the Limited memory Broyden-Fletcher-Goldfarb-Shanno （L-BFGS） with chaotic model into EM. The main idea of the algorithm was using the L-BFGS which has high precision, in the later stage of algorithm, and using the chaotic model through the whole algorithm to keep the diversity of population. The tests suggested that the algorithm could jump out from the local optimal solution, had better solution and converged faster than EM, Particle Swarm Optimization （PSO） and particle swarm optimization with Time-Varying Accelerator Coefficients （TVAC）. Tests also showed that it could be used in path planning and had better results than both PSO and Ant Colony Optimization （ACO）, so the algorithm can be applied to the discrete domain question.