文献类型：期刊文献

中文题名：一种求解高维多模态复杂问题的差分文化算法

英文题名：A novel differential evolution based cultural algorithm for solving high-dimensional multimodal optimization problems

作者：拓守恒[1];陶维天[2]

第一作者：拓守恒

机构：[1]陕西理工学院数学与计算机科学学院,陕西汉中723000;[2]甘肃中医学院网络中心,甘肃兰州730000

第一机构：陕西理工学院数学与计算机科学学院,陕西汉中723000

年份：2013

卷号：35

期号：1

起止页码：142

中文期刊名：计算机工程与科学

外文期刊名：Computer Engineering & Science

收录：CSTPCD;;北大核心:【北大核心2011】；CSCD:【CSCD2013_2014】；

语种：中文

中文关键词：高维多模态问题;自适应差分进化;高斯分布估计算法;小生境;文化算法

外文关键词：high-dimensional multimodal ~ adaptive differential evolution ; Gaussian estimation of dis-tribution algorithm ; nicheelite ~ cultural algorithm

摘要：针对在求解高维多峰值复杂问题时种群容易陷入局部搜索、求解精度低的问题,提出了一种基于自适应差分进化算法和小生境高斯分布估计的文化算法。将差分进化算法用于种群空间的优化,利用动态小生境识别算法在种群空间中识别小生境群体。信度空间利用高斯分布估计算法在小生境内进行局部优化,并将小生境特征存入进化知识库,进化知识库进一步引导种群空间,有效地保证了种群的多样性,避免了局部的重复搜索。最后,通过仿真实验测试表明,算法具有收敛速度快、求解精度高、稳定性高和全局搜索能力强等优势。
Aiming at the defects of slow rate of convergence and easily falling into local optimum in the traditional evolution algorithm, a self-adaptive Cultural Algorithm （CA） based on Differential Evo- lution （DE） and niche elite Gaussian Estimation of Distribution Algorithm is proposed to resolve high-di- mensional multimodal optimization problems. The self-adaptive differential evolution algorithm is used to optimize the population space and the niche elite population is recognized by dynamic recognition algo- rithm. In the belief space, the niche elite population is optimized by Gaussian Estimation of Distribution Algorithm. The optimized result and the size and characteristics of the niche are stored into the evolution knowledge base. Then, the population in the population space is guided and inspired by the evolution knowledge base. It guarantees population diversity and avoids the duplication of local search. Finally, this algorithm is tested on 4 multimodal benchmark functions, and the experimental result shows the al- gorithm has advantages in convergence velocity, solution precision, stabilization and global search capa- bility.