摘要
蚁群算法的离散本质限制了其在连续问题求解中的应用,针对该问题提出求解连续函数优化问题的连续蚁群优化算法。对概率密度呈高斯分布的分布函数进行随机采样,为每只蚂蚁产生下一步迭代的M(M≥2)个候选位置,引入记忆表取代基本蚁群算法中的禁忌表,通过对记忆表中的优良解进行动态替换实现信息素更新。与其他连续优化算法的比较结果证明,该算法在复杂度、稳定性等方面具有优势。
Aiming at the problem that the ant colony algorithm is limited in solving continuous problem by its discrete quality,this paper presents an Ant Colony Optimization algorithm（ACO） for continuous domain.A random generator is used with Gaussian distribution to sample and generate M（M ≥ 2） candidate points for every ant.Function of the tabu list in the ACO is replaced by the memory table.The pheromone update is accomplished by replacing the good solutions in the memory table dynamically.Compared with other continuous optimization methods,this algorithm has satisfactory performance in aspects of complexity and stability.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第16期183-185,共3页
Computer Engineering
基金
国家自然科学基金资助项目(60874070)
高校博士点基金资助项目(20070533131)
关键词
蚁群优化算法
高斯分布
记忆表
ant colony optimization algorithm
Gaussian distribution
memory table
