摘要
通过分布熵和方差的对比分析,建立群搜索算法中多样性的定量描述.针对优化计算中的多模态情况提出个体空间中的模式分类问题,并提出一种分类方法.在聚类分析的基础上得到搜索空间中个体的类分布,进而得到由分布熵描述的多样性指标,并据此控制个体间的聚散来实现对多样性的控制.给出一种控制多样性的一阶聚散控制算法,对其参数设置进行分析.仿真实验表明该算法优于标准遗传算法、标准粒子群算法以及无分类过程的集聚性搜索算法.
A quantitative description of diversity in population-based search algorithms is put forward by comparing distribution entropy with variance. The problem of mode classification in individual space is presented for multimodal cases in optimization computation, and a classification method is proposed. On the basis of clustering analysis, the class distribution of individuals in search space is acquired. Furthermore, the diversity index described by distribution entropy is obtained. Then, diversity control is implemented by aggregation and dilation among individuals according to diversity. As an example, a first-order aggregation and dilation （A＆D） algorithm for diversity control is presented and the setting of its parameters is analyzed. Simulation results demonstrate that the proposed algorithm performs better than the canonical genetic algorithm, the particle swarm optimization and the A＆D search algorithm without classification.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2009年第3期374-380,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60374069)
关键词
群搜索优化
多样性
分布熵
模式分类
集聚与扩散
Population-Based Search and Optimization, Diversity, Distribution Entropy, ModeClassification, Aggregation and Dilation
