基于云模型的小生境MAX-MIN相遇蚁群算法

针对基本蚁群算法在解决大规模优化问题时易限于局部最优解、收敛速度慢的突出缺陷,本文在阐述基本蚁群算法和云模型理论的基础上,提出了一种利用云模型定性关联规则来有效限制基本蚁群算法陷入局部最优解的方法;随后借助最优解保留、相遇搜索和信息素自适应控制策略以及自然界的小生境思想对基本蚁群算法进行了系列改进,以提高改进后蚁群算法的全局收敛性能。同时,为了避免蚁群在搜索过程中易出现停滞现象,将各条寻优路径上可能的残留信息素数量限制在一个最大最小区间。仿真实验结果验证了本文所提改进蚁群算法的可行性和有效性。

MAX-MIN meeting ant colony algorithm based on cloud model theory and niche ideology

Ant colony algorithm(ACA) is easy to fall in local best,and its convergent speed is slow in solving large-scale optimization problems.On the basis of introduction of basic ant colony algorithm and cloud model theory,a novel qualitative strategy for improving the global optimization properties by use of cloud models is presented in this paper.Then,for the purpose of enhancing global convergent performance of basic ant colony algorithm,the basic ant colony algorithm is improved by using elitist preservation strategy,meeting search strategy,pheromone adaptive control strategy and natural niche ideology.Meanwhile,in order to avoid stagnation of the search,the range of possible pheromone trails on each solution component is limited to a maximum-minimum interval.The feasibility and effectiveness of the proposed ant colony algorithm are validated by series of computational experiments.