摘要
由于蚂蚁寻优路径上函数解信息量的差异容易导致蚂蚁在高信息函数解位置聚拢影响计算效果,因此提出基于信息熵策略优化的自适应蚁群算法。计算出蚂蚁行进路径上可能会误导蚂蚁出现局部聚集的函数解,结合其被选择的概率,对该影响蚂蚁路径选择函数解对应的信息权重进行差异化赋权,确保蚂蚁在经过任意路径上的任意一个函数解位置时,重新对下一位置的函数解重新进行评估,避免受到局部最优解的干扰,结合函数解之间的信息素相似贡献率,将最短路径作为蚂蚁寻优的标准。测试结果表明,设计算法能够得到全局最优解,且具有较良好的收敛性。
Due to the difference in the amount of information of the function solution on the ant optimization path, it is easy to cause the ants to gather at the position of the high information function solution and affect the calculation effect. Therefore, an adaptive ant colony algorithm based on information entropy strategy optimization is proposed. Calculate the function solution that may mislead the ants to appear local aggregation on the ant’s travel path, and combined with the probability of being selected, give differential weight to the information weight corresponding to the function solution affecting the ant’s path selection, so as to ensure that when the ant passes through any position of the function solution on any path, the function solution at the next position is re evaluated, Avoiding the interference of local optimal solution, combined with the pheromone similarity contribution rate between function solutions, the shortest path is taken as the standard of Ant Optimization. The test results show that the design algorithm can obtain the global optimal solution and has good convergence.
作者
黄美霞
HUANG Meixia(College of Mathematics and Information Engineering,Puyang Vocational and Technical College,Puyang Henan 457000,China)
出处
《信息与电脑》
2022年第2期87-89,共3页
Information & Computer
关键词
信息熵策略优化
自适应
蚁群算法
局部聚集
信息权重
局部最优解
information entropy strategy optimization
adaptive
ant colony algorithm
local aggregation
information weight
local optimal solution
作者简介
黄美霞(1981-),女,河南濮阳人,本科,讲师。研究方向:数学教育。
