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基于云模型的小生境MAX-MIN相遇蚁群算法
Alternative TitleMAX2M IN meeting ant colony a lgor ithm ba sed on cloud modeltheory and n iche ideology
段海滨; 王道波; 于秀芬; 北京8701信箱
Department国家863计划微波遥感技术实验室
Source Publication吉林大学学报:工学版
2006
Volume36Issue:5Pages:803-808
ISSN1671-5497
Language中文
Keyword人工智能 蚁群算法 信息素 云模型 定性关联规则 小生境
Abstract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.
Other AbstractAnt colony algorithm (ACA) is easy to fall in local best, and its convergent speed is slow in solving large-scale op timization p roblems.On the basis of introduction of basic ant colony algorithm and cloud model theory,a novel qualitative strategy for imp roving the global op timization p roperties by use of cloud models is presented in this paper.Then, for the purpose of enhancing global convergent performance of basicant colony algorithm, the basic ant colony algorithm is imp roved by using elitist p reservation strategy, meeting search strategy, pheromone adap tive 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.
Indexed ByCSCD
Funding Project中国科学院空间科学与应用研究中心
Citation statistics
Cited Times:10[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/1938
Collection微波遥感部
Corresponding Author北京8701信箱
Recommended Citation
GB/T 7714
段海滨,王道波,于秀芬,等. 基于云模型的小生境MAX-MIN相遇蚁群算法[J]. 吉林大学学报:工学版,2006,36(5):803-808.
APA 段海滨,王道波,于秀芬,&北京8701信箱.(2006).基于云模型的小生境MAX-MIN相遇蚁群算法.吉林大学学报:工学版,36(5),803-808.
MLA 段海滨,et al."基于云模型的小生境MAX-MIN相遇蚁群算法".吉林大学学报:工学版 36.5(2006):803-808.
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