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Flood Risk Zoning Model Based on Ant-Miner and Its Application

LAI Chengguang
,WANG Zhaoli,CHEN Xiaohong, HUANG Ruizhen, LIAO Weilin,WU Xushu

- , 2015,
Abstract: 应用蚁群优化算法(Ant Colony Optimization, ACO)进行规则挖掘是一个新的研究热点。为解决指标变量与风险级别间非线性关系,提出一种基于蚁群规则挖掘算法(AntMiner)的洪灾风险区划模型。在GIS技术支持下,将该模型应用于北江流域洪灾风险区划实例中,结果表明:① Ant-Miner模型可挖掘15条适合研究区的洪灾风险分类规则,这些规则以简单的条件语句形式表现,便于生成风险区划图;② Ant-Miner模型测试精度(95.1%)高于相同条件下BP神经网络模型的精度(92.9%),表明其分类性能更好,对洪灾风险区划具有更好的适用性;③ 研究区高风险区主要集中于降雨量较大、地势平缓低洼、人口财产密集的地区,与历史洪灾风险情况较吻合,表明所构建的模型科学合理,可为流域洪灾风险评价提供了新思路
Data classification by Fuzzy Ant-Miner
Mohamed Hamlich,Mohammed Ramdani
International Journal of Computer Science Issues , 2012,
Abstract: In this paper we propose an extension of classification algorithm based on ant colony algorithms to handle continuous valued attributes using the concepts of fuzzy logic. The ant colony algorithms transform continuous attributes into nominal attributes by creating clenched discrete intervals. This may lead to false predictions of the target attribute, especially if the attribute value history is close to the borders of discretization. Continuous attributes are discretized on the fly into fuzzy partitions that will be used to develop an algorithm called Fuzzy Ant-Miner. Fuzzy rules are generated by using the concept of fuzzy entropy and fuzzy fitness of a rule.
S. Murugan,,Dr.S. Radhakrishnan
International Journal of Engineering Science and Technology , 2010,
Abstract: Myocardial ischemia is the most common cardiac disease and is characterized by a high risk of sudden cardiac death. The accurate ischemic episode detection, where a sequence of cardiac beats is assessed, is based on the correct detection of ischemic beats. In this paper, a data mining approach, association rule based classification using Ant-Miner algorithm is proposed for automatic detection of ischemic ECG beats. The proposed work has two steps: initially the noise is removed and the features are extracted from the ECG signals. With these features, the Ant-Miner is applied to extract the rules. European Society of Cardiology ST-T database of ECGbeats is used to analyze the performance of our proposed method with the existing. The greater accuracy of the proposed method shows its high performance than the existing.
Classification of Remote Sensing Images based on Ant Colony Optimization

LIU Xiao-ping,LI Xi,HE Jin-qiang,AI Bin,PENG Xiao-juan,

遥感学报 , 2008,
Abstract: This paper presents a bottom-up approach to mi prove the classification performance for remote sensing applications. Top-down approaches, such as statistical classifiers, have inherited lmi itations in dealingwith complicated relationships in classification. Forexample, data correlation between bandsofremote sensing mi ageryhas caused problems in generating satisfactory classification with statisticalmethods. In this paper, ant colony optmi ization (ACO) based on swarm intelligence is used to mi prove classification performance. Actually, ACO is a complex multi-agent system, in which agentswith smi ple intelligence can complete complex tasks through cooperation such as classification problems. Ants guide their route selection based on pheromone, which is accumulated from the collectivemovements of individual ants. In thisway, an ant learns from the pastexperience ofothers. Ant-Miner is different from decision tree approaches. The entropymeasure is a local heuristicmeasure, which considers only one attribute at a tmi e, and so it is sensitive to attribute correlation problems. Whereas inAnt-Miner, pheromone updating tends to cope betterwith attribute correlation, since pheromone updating is directly based on the performance of the rule as awhole. Thus, Ant-Miner should have great potential in mi proving remote sensing classification. In this study, an Ant-Miner program for discovering classification rules is developed for the classification of remote sensing mi ages. In theAnt-Miner program, the route search by an ant colony is to find the best links between attribute nodes and class nodes. An attribute node corresponds to a band value of remote sensing mi ages. An attribute node can only be selected once andmustbe associated with a class node. Each route corresponds to a classification rule, and discovering a classification rule can be regarded as searching for an optmi al route. To enableACO to effectively classify remote sensing mi agery of very large data sets, original band values are sliced into a number of intervals by using a discretization technique. The ACO method is more explicit and comprehensible than mathematical equations. Our study in Guangzhou city indicates that the ant colony-based classifier yields better accuracy than conventionalmaxmi um likelihood classifiers and decision tree classifiers. The overall accuracy of theACO method is 88.6%, with aKappa coefficientof0.861. The decision treemethod has an accuracy of85.4% and aKappa coefficientof 0.822. Themaxmi um likelihoodmethod has an accuracy of83. 3% and aKappa coefficient of0. 796. The results clearly support the conclusion that themethod explored in thispapercan bemore effective than conventional classificationmethods.
ACO-Steiner: Ant Colony Optimization Based Rectilinear Steiner Minimal Tree Algorithm
Yu Hu,Tong Jing,Zhe Feng,Xian-Long Hong,Xiao-Dong Hu,Gui-Ying Yan,
Yu Hu
,Tong Jing,Zhe Feng,Xian-Long Hong,Xiao-Dong Hu,and Gui-Ying Yan

计算机科学技术学报 , 2006,
Abstract: The rectilinear Steiner minimal tree (RSMT) problem is one of the fundamental problems in physical design, especially in routing, which is known to be NP-complete. This paper presents an algorithm, called ACO-Steiner, for RSMT construction based on ant colony optimization (ACO). An RSMT is constructed with ants' movements in Hanan grid, and then the constraint of Hanan grid is broken to accelerate ants' movements to improve the performance of the algorithm. This algorithm has been implemented on a Sun workstation with Unix operating system and the results have been compared with the fastest exact RSMT algorithm, GeoSteiner 3.1 and a recent heuristic using batched greedy triple construction (BGTC). Experimental results show that ACO-Steiner can get a short running time and keep the high performance. Furthermore, it is also found that the ACO-Steiner can be easily extended to be used to some other problems, such as rectilinear Steiner minimal tree avoiding obstacles, and congestion reduction in global routing.
A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix  [cached]
FuQing Zhao,JianXin Tang,YaHong Yang
Journal of Computers , 2012, DOI: 10.4304/jcp.7.3.736-742
Abstract: Manufacturing supply chain(SC) faces changing business environment and various customer demands. Pareto Ant Colony Optimisation (P-ACO) in order to obtain the non-dominated set of different SC designs was utilized as the guidance for designing manufacturing SC. P-ACO explores the solution space on the basis of applying the Ant Colony Optimisation algorithm and implementing more than one pheromone matrix, one for every objective. The SC design problem has been addressed by using Pareto Ant Colony Optimisation in which two objectives are minimised simultaneously. There were tested two ways in which the quantity of pheromones in the PM is incremented. In the SPM, the pheromone increment is a function of the two objectives, cost and time, while in MPM the pheromone matrix is divided into two pheromones, one for the cost and another one for the time. It could be concluded that the number of solutions do not depend on if the pheromone is split or is a function of the two variables because both method explore the same solution space. Although both methods explore the same solution space, the POS generated by every one is different. The POS that is generated when the pheromone matrix is split got solutions with lower time and cost than SMP because in the probabilistic decision rule a value of λ = 0.2 is used. It means that the ants preferred solution with a low cost instead of solutions with low time. The strategy of letting the best-so-far ant deposit pheromone over the PM accelerates the algorithm to get the optimal POS although the number of ants in the colony is small. An experimental example is used to test the algorithm and show the benefits of utilising two pheromone matrices and multiple ant colonies in SC optimisation problem.
New ant colony optimization for load-balancing and routing mechanism

XIA Hong-bin a,b,XU Wen-bo b,LIU Yuan a,

计算机应用研究 , 2009,
Abstract: This paper proposed and implemented a new dynamic transition and search strategy for load-balancing and routing. Partitioned the artificial ants into several groups. Each subgroup of ant colony released different types of pheromones. Introduced attract factor and exclusion factor,and gave a new transition probability with multiple ant colony,so as to strengthen the global search capability. Proposed three mechanisms based on ACO for load-balancing and routing. The performance of the proposed three mechanism...
An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem
Alena Shmygelska, Holger H Hoos
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-30
Abstract: We present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations.The application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise.Ant Colony Optimisation (ACO) is a population-based stochastic search method for solving a wide range of combinatorial optimisation problems. ACO is based on the concept of stigmergy – indirect communication between members of a population through interaction with the environment. An example of stigmergy is the communication of ants during the foraging process: ants indirectly communicate with each other by depositing pheromone trails on the ground and thereby influencing the decision processes of other ants. This simple form of communication between individual ants gives rise to complex behaviours and capabilities of the colony as a whole.From the computational point of view, ACO is an iterative construction search method in which a population of simple agents ('ants') repeatedly constructs candidate solutions to a given problem; this construction process is probabilistically guided by heuristic information on the given problem instance as well as by a shared memory containing experience gathered by the ants in previous iteration
Optimization of Side Lobe Level and Fixing Quasi-Nulls in Both of the Sum and Difference Patterns by Using Continuous Ant Colony Optimization (ACO) Method
S. Ali Hosseini;Zahra Atlasbaf
PIER , 2008, DOI: 10.2528/PIER07102901
Abstract: In this paper, the optimization of both sum and difference patterns of linear monopulse antennas with low side lobe levels, high directivity and also narrow main beam width are efficiently solved by Continuous Ant Colony Optimization (ACO) Method. The synthesis problem is optimized by defining a suitable cost function which is based on limitation of the side lobe level. In this work, three different parameters are considered to be optimized separately which are the excitation amplitude of each element, the excitation phase of each element and finally the element-to-element spacing. Numerical results of each step, sum and difference patterns, are illustrated in each related part. Finally, we investigate placing some nulls in specific directions to suppress the jamming signals in both sum and difference patterns.
ACO-ESSVHOA - Ant Colony Optimization based Multi-Criteria Decision Making for Efficient Signal Selection in Mobile Vertical Handoff  [PDF]
A. Bhuvaneswari,E. George Dharma Prakash Raj,V. Sinthu Janita Prakash
Computer Science , 2014,
Abstract: The process of Vertical handoff has become one of the major components of today's wireless environment due to the availability of the vast variety of signals. The decision for a handoff should be performed catering to the needs of the current transmission that is being carried out. Our paper describes a modified Ant Colony Optimization based handoff mechanism, that considers multiple criteria in its decision making process rather than a single parameter (pheromone intensity). In general, ACO considers the pheromone intensity and the evaporation rates as the parameters for selecting a route. In this paper, we describe a mechanism that determines the evaporation rates of each path connected to the source using various criteria, which in turn reflects on the pheromone levels present in the path and hence the probability of selecting that route. Experiments show that our process exhibits better convergence rates, hence better usability.
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