摘要
本文结合二次分配问题(quadratic assignment problem,QAP)的特点,通过分析传统蚂蚁算法在解决QAP问题时收敛过快,精度不高的缺点,提出一种以ACS(ant colony system)为基础的改进蚁群算法――信息素迭代累积ACS(ACS with accumu-lated pheromone by iteration,ACS_API)。新方法通过对定义启发式信息和信息素更新规则的改进,扩大了搜索空间,从而避免过早收敛,陷入局部最优解中。该算法已应用于QAP标准测试数据,并通过与另外两种先前提出的改进蚂蚁算法(HAS_QAP,ACO_GLS)的比较分析得出了它在算法精度和执行时间上的优势。
Through analyzing traditional ant algorithm＇s weakness of premature convergence and low precision and the features of QAP（quadratic assignment problem）,this paper proposes an improved ant algorithm based on ACS（ant colony system）,which is called ACS with accumulated pheromone by iteration（ACS_API）.According to redefining heuristic information and improving updating rules,this new method avoids premature convergence and local optimal solutions by broadening its search space.Finally,the ACS_API is applied to standard test data of QAP.After compared with other two ant algorithms proposed before,the results show that ACS_API is superior to those two methods no mater in computing time or precision.
出处
《微计算机信息》
2010年第15期182-183,141,共3页
Control & Automation
