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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
Source Publication吉林大学学报:工学版
Keyword人工智能 蚁群算法 信息素 云模型 定性关联规则 小生境
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
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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