摘要
蚁群算法中的信息素更新对整个算法的性能起到决定性的作用。在对蚁群算法进行系统仿真实验过程中,发现存在多种不确定因素影响信息素的更新。粗糙集是一种处理不确定和模糊知识的工具,本文利用粗糙集对试验结果进行了分析,给出了不确定因素之间的关系,并根据分析结果对信息素更新策略作了相应的改进,提高了算法的性能。
The pheromone updating is a deterministic factor in the performance of ant colony optimization algorithm （ACO）. During the ACO system emulation experiment, many uncertain factors of pheromone updating are found. The rough sets approach is an important tool to deal with uncertain or vague knowledge. A lot of experimental results are analyzed and the relationships between uncertain factors are given. According to the results strategy of pheromone updating and performance of ACO are improved.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第24期6337-6339,6343,共4页
Computer Engineering and Design
关键词
蚁群算法
信息素更新
仿真
不确定因素
粗糙集
ant colony optimization algorithm
pheromone updating
simulation
uncertain factors
rough sets
