南京师范大学数学与计算机科学学院,江苏,南京,210097
纸质出版:2008
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马 卫, 朱庆保. 求解函数优化问题的快速连续蚁群算法[J]. 电子学报, 2008,36(11):2120-2124.
MA Wei, ZHU Qing-bao. Fast Continuous Ant Colony Optimization Algorithm for Solving Function Optimization Problems[J]. Acta Electronica Sinica, 2008, 36(11): 2120-2124.
用蚁群算法进行函数优化时
存在收敛速度慢并易于陷入局部最小等问题.为此
根据对真实蚂蚁的最新研究成果
提出了一种全新的由侦察蚁和觅食蚁协作搜索的函数优化快速连续蚁群算法.该算法首先引入混沌序列确定侦察蚁的初始位置
然后由侦察蚁进行全局大视域快速搜索
且每迭代完一步和每迭代完一代都要对解进行评价
并对本代最优解的信息素进行标记
由此吸引觅食蚁在本代最优解周围空间进行小步长搜索.通过这种初始化方法和侦察蚁与觅食蚁的相互协作
不仅能很好的提高寻优精度
且使收敛速度大幅提高.计算机仿真实验结果表明
本算法寻优率高
收敛速度提高显著
效果令人满意.
Using ant colony algorithm to solve function optimization problems has some disadvantages such as easily plunging into a local minimum
slow convergence speed and so on.Therefore
a new fast continuous ant colony optimization algorithm is presented according to the latest research achievements of ant's behavior
which is carried out by scout ants and foraging ants cooperating with each other to search the best solution for solving function optimization problems. In our algorithm
chaotic sequence is first introduced to determine the initial position of the scout ants
then the scout ants start global rapid search in large visual field.In order to achieve better performance
it needs to evaluate solutions in each step and each generation and mark pheromones of the optimal solution in this generation.Thus foraging ants are attracted around the optimal solution during this generation to search in small step.Through this initialization method and mutual cooperation between the two kinds of ants
it could not only improve the optimization accuracy
but improve convergence speed greatly.The computer simulation experiments show that the algorithm has high search efficiency and rapid convergence speed.The results are quite satisfactory.
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