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由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化(Joint Strength based Ant Colony Optimization,JSACO)聚类算法,该算法引入了连接强度的概念对蚁群聚类算法中的拾起/放下规则加以改进,以连接强度作为拾起规则,对结点进行聚类,并根据放下规则放弃部分不良数据,产生最终聚类结果.最后采用了MIPS数据库中的PPI数据进行实验,将JSACO算法与PPI网络数据的其他聚类算法进行比较,聚类结果表明JSACO算法正确率高,时间开销低. 相似文献
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交叉变异的连续蚁群优化算法 总被引:3,自引:2,他引:1
研究了应用于连续空间优化问题的蚁群算法,给出了信息素的留存方式以及搜索策略.另外,针对蚁群算法易陷入局部最优的缺点,在最优蚂蚁周围进行了精细搜索,并加入了自适应的交叉变异算子,从而改进了蚁群算法的全局优化性能.数值仿真结果表明,该算法是一种有效的优化算法. 相似文献
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Nowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high‐performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state‐of‐the‐art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate‐in‐nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO‐CLA is proposed to solve task scheduling problem in the mesh‐topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO‐CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second‐best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO‐CLA is 2.66% and 5.15% better than the ETF (the second‐best approach) and the average performance of all the other competitors. 相似文献
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An accurate estimation of aquifer hydraulic parameters is required for groundwater modeling and proper management of vital groundwater resources. In situ measurements of aquifer hydraulic parameters are expensive and difficult. Traditionally, these parameters have been estimated by graphical methods that are approximate and time-consuming. As a result, nonlinear programming (NLP) techniques have been used extensively to estimate them. Despite the outperformance of NLP approaches over graphical methods, they tend to converge to local minima and typically suffer from a convergence problem. In this study, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) methods are used to identify hydraulic parameters (i.e., storage coefficient, hydraulic conductivity, transmissivity, specific yield, and leakage factor) of three types of aquifers namely, confined, unconfined, and leaky from real time–drawdown pumping test data. The performance of GA and ACO is also compared with that of graphical and NLP techniques. The results show that both GA and ACO are efficient, robust, and reliable for estimating various aquifer hydraulic parameters from the time–drawdown data and perform better than the graphical and NLP techniques. The outcomes also indicate that the accuracy of GA and ACO is comparable. Comparing the running time of various utilized methods illustrates that ACO converges to the optimal solution faster than other techniques, while the graphical method has the highest running time. 相似文献
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提出基于虚拟阻抗的动态等效模型在线修正方法。首先,指出等值过程中不合理的聚类算法、对时变系统的定常化假设是误差主要来源。接着,提出通过等效模型的在线修正以克服系统时变性所导致的误差。由于等效模型可调参数过多,难以对所有参数进行调整。为此,在等效发电机、等效电动机节点引入附加虚拟阻抗,应用蚁群优化算法进行调节,以实现边界点最佳功率匹配,并利用广域测量系统(wide area measurement system,WAMS)提供的实测数据对动态等效模型进行定时修正。最后,IEEE 10机39母线系统的等值计算结果表明:算法较好地改进了动态等效模型的静态精度与暂态精度,提高了模型的强壮性。 相似文献
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Melt index (MI) is a crucial indicator in determining the product specifications and grades of polypropylene (PP). The prediction of MI, which is important in quality control of the PP polymerization process, is studied in this work. Based on RBF (radial basis function) neural network, a soft‐sensor model (RBF model) of the PP process is developed to infer the MI of PP from a bunch of process variables. Considering that the PP process is too complicated for the RBF neural network with a general set of parameters, a new ant colony optimization (ACO) algorithm, N‐ACO, and its adaptive version, A‐N‐ACO, which aim at continuous optimizing problems are proposed to optimize the structure parameters of the RBF neural network, respectively, and the structure‐best models, N‐ACO‐RBF model and A‐N‐ACO‐RBF model for the MI prediction of propylene polymerization process, are presented then. Based on the data from a real PP production plant, a detailed comparison research among the models is carried out. The research results confirm the prediction accuracy of the models and also prove the effectiveness of proposed N‐ACO and A‐N‐ACO optimization approaches in solving continuous optimizing problem. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010 相似文献
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Shengnan Lv Yang Zheng Mengmeng Yu Lin Shen Jiping Sheng 《Journal of the science of food and agriculture》2013,93(5):1003-1009
BACKGROUND: Mitogen‐activated protein kinase (MAPK, EC 2.7.11.24) cascade from several plant species has been shown to be activated during response to abiotic stress. Ethylene plays an important role in fruit tolerance to environmental stress. However, the mechanisms by which MAPK regulates defence systems in fruit and the relationship between MAPK and ethylene remain to be determined. RESULTS: MAPK inhibitor significantly decreased the chilling tolerance of tomato (Lycopersicon esculentum cv. Lichun) fruit during cold storage. Moreover, decreases in ethylene content, LeACS2 expression and activities of 1‐aminocyclopropane‐1‐carboxylic acid (ACC) synthase (ACS, EC 4.4.1.14) and ACC oxidase (ACO, EC 1.14.17.4) due to MAPK inhibitor occurred within 24 h after cold treatment. Upon treatment with cold and ethephon, the ethylene content, activities of ACS and ACO and expression of LeACS2, LeACO1 and LeMAPK4 increased. CONCLUSION: The results showed the regulation of MAPK in ethylene biosynthesis to protect tomato fruit from cold stress. In addition, the participation of LeMAPK4 in cold‐induced ethylene biosynthesis in tomato fruit was indicated. © 2013 Society of Chemical Industry 相似文献