摘要
针对蚁群算法在解决大规模优化问题中易陷入局部最优及收敛速度慢等缺陷,提出了一种基于云模型的自适应蚁群算法。通过对信息素分布状况进行评价,自适应地调整云模型中各参数,并根据云模型参数,确定全局最优及次优路径,进行全局信息素更新,以改善算法的全局搜索能力。同时,为了避免算法陷入停滞,将信息素大小限制在一个最大最小区间。仿真实验结果验证了提出的算法的高效性和稳定性。
Ant colony algorithm is easy to fall in local best, and its convergent speed is slow in solving large-scale optimiza-tion problems. In this paper, a self-adaptive ant colony algorithm based on cloud model is proposed. Through the evaluation of pher-omone distribution, the parameters of the cloud model are adjusted adaptively. According to the parameters, the global optimal and suboptimal paths are determined, and the global pheromone is updated to improve the global search ability of the algorithm. Mean-while, in order to avoid stagnation, the range of pheromone is limited to a maximum-minimum interval. Simulation results validate the efficiency and stability of the proposed algorithm.
出处
《阜阳师范学院学报（自然科学版）》
2015年第2期87-91,共5页
Journal of Fuyang Normal University(Natural Science)
基金
安徽省教育厅自然科学基金项目(2014KJ021
2014KJ023)
阜阳师范学院自然科学基金项目(2013FSKJ02ZD
2014FSKJ09)
阜阳师范学院大学生创新训练项目(FS201310371122)
阜阳师范学院质量工程项目(2013ZYSD05
2014JXTD01)资助
关键词
蚁群算法
云模型
信息素
旅行商问题
ant colony algorithm
cloud model
pheromone
traveling salesman problem
