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.
出处
《计算机工程与科学》
CSCD
北大核心
2013年第1期142-148,共7页
Computer Engineering & Science
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
high-dimensional multimodal ~ adaptive differential evolution
Gaussian estimation of dis-tribution algorithm
nicheelite ~ cultural algorithm
