A Novel Ant based Clustering of Gene Expression Data using MapReduce Framework

Genes which exhibit similar patterns are often functionally related. Microarray technology provides a unique tool to examine how a cells gene expression pattern chang es under various conditions. Analyzing and interpreting these gene expression data is a challenging task. Clustering is one of the useful and popular methods to extract useful patterns from these gene expression data. In this paper multi colony ant based clustering approach is proposed. The whole processing procedure is divided into two parts: The first is the construction of Minimum spanning tree from the gene expression data using MapReduce version of ant colony optimization techniques. The second part is clustering, which is done by cutting the costlier edges from the minimum spanning tree, followed by one step k - means clustering procedure. Applied to different file sizes of gene expression data over different number of processors, the proposed approach exhibits good scalability and accuracy

Hybrid ACO and TOFA feature selection approach for text classification

With the highly increasing availability of text data on the Internet, the process of selecting an appropriate set of features for text classification becomes more important, for not only reducing the dimensionality of the feature space, but also for improving the classification performance. This paper proposes a novel feature selection approach to improve the performance of text classifier based on an integration of Ant Colony Optimization algorithm (ACO) and Trace Oriented Feature Analysis (TOFA). ACO is metaheuristic search algorithm derived by the study of foraging behavior of real ants, specifically the pheromone communication to find the shortest path to the food source. TOFA is a unified optimization framework developed to integrate and unify several state-of-the-art dimension reduction algorithms through optimization framework. It has been shown in previous research that ACO is one of the promising approaches for optimization and feature selection problems. TOFA is capable of dealing with large scale text data and can be applied to several text analysis applications such as text classification, clustering and retrieval. For classification performance yet effective, the proposed approach makes use of TOFA and classifier performance as heuristic information of ACO. The results on Reuters and Brown public datasets demonstrate the effectiveness of the proposed approach. © 2012 IEEE

Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions

With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAX-MIN ant systems on the class of linear pseudo-Boolean functions defined on binary strings of length 'n'. Our investigations point out how the progress according to function values is stored in pheromone. We provide a general upper bound of O((n^3 \log n)/ \rho) for two ACO variants on all linear functions, where (\rho) determines the pheromone update strength. Furthermore, we show improved bounds for two well-known linear pseudo-Boolean functions called OneMax and BinVal and give additional insights using an experimental study.Comment: 19 pages, 2 figure

An improved ant colony optimization-based approach with mobile sink for wireless sensor networks

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms

Hastane Yönetim Etkinliğinde Yerleşim Planının Önemi ve Bir Model Çalışması

Günümüzde talebin gittikçe arttığı ve her gün daha fazla insanın sağlık hizmeti alabilmek için başvurduğu sağlık kuruluşları, fiziksel olarak çok geniş alanları kaplamakta ve çoğalan uzmanlık alanları sebebiyle gittikçe artan sayıda poliklinik ve laboratuar birimleri ile hizmet vermektedirler. Niteliksel olarak önemli gelişme gösteren ve başvuru sayılarının hızla arttığı hastanelerde, hastaların hastane içindeki birimlere ulaşımları büyük ve beklenmeyen sorunlar oluşturmaya başlamış ve hastanelerde biriken yoğun kalabalıklar bu durumu kronik bir sorun haline getirmiştir. Geçmiş dönemlerde hastaların muayene sonrası tanı birimlerine yönlendirilme oranları çok yüksek değil iken, günümüzde teşhis amaçlı yapılan birçok muayene, tanı birimlerinden alınan sonuçlarla desteklenmektedir. Benzer şekilde poliklinikler arası konsültasyon istem sayıları da fazlalaşmış, sağlık kurumları içerisinde birimler arası ulaşım ve etkileşim sıklıkla yapılır hale gelmiştir. Bu durum, hastane yöneticilerini, hastane mekân organizasyonu ve özellikle poliklinik, laboratuar ve radyoloji birimlerinin hastane içerisindeki yerleşim düzenleri üzerinde planlama yapmaya sevk etmiştir. Bu çalışmada, en uygun hastane yerleşim planlarını oluşturarak hastane içi ulaşım problemlerini en aza indirebilmek amacıyla karınca kolonisi algoritması temelinde bir yazılım geliştirilmiş ve bu yazılım vasıtasıyla en uygun hastane yerleşim planlarının oluşturulabilmesi için bir model önerisi yapılmıştır. Çalışma sonuçlarına göre, örnek modelde, poliklinik hastalarının ilk başvuru yapabilmek için gerçekleştirdikleri hastane içi sirkülasyonda %62, konsültasyon istemlerinde gerçekleşen hastane içi sirkülasyonda %78, polikliniklerden laboratuarlara gönderilen hastaların ulaşımında %23, polikliniklerden radyoloji birimlerine gönderilen hastaların ulaşımında ise %53 oranlarında kazanım sağlanmıştır. Çalışma kapsamında geliştirlen model önerisi, özellikle mekansal olarak çok geniş alanlarda hizmet veren, çok sayıda uzmanlaşmış poliklinik ve labaratuvara sahip ve her gün binlerce hastanın başvurduğu sağlık kurumlarının poliklinik, labaratuvar ve radyoloji birimlerinin mümkün olan en doğru şekilde konumlandırılabilmesi için yön gösterici olabilecektir. Çalışmada geliştirlen sistem, hastanede hizmet veren birimlerin tercih edilen fiziksel büyüklüklerini, poliklinik konsültasyon sayılarını ve polikliniklerden labaratuvar ve radyoloji birimlerine parametrik olarak aldığı için farklı çalışma ve iş düzenine veya farklı fiziksel tasarıma sahip olan sağlık kurumlarında kolaylıkla uygulanabilecektir

Ant colony optimization and the minimum spanning tree problem

Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization. Solutions for a given problem are constructed by a random walk on a so-called construction graph. This random walk can be influenced by heuristic information about the problem. In contrast to many successful applications, the theoretical foundation of this kind of metaheuristic is rather weak. Theoretical investigations with respect to the runtime behavior of ACO algorithms have been started only recently for the optimization of pseudo-Boolean functions. We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem. In our investigations, we consider the minimum spanning tree problem and examine the effect of two construction graphs with respect to the runtime behavior. The choice of the construction graph in an ACO algorithm seems to be crucial for the success of such an algorithm. First, we take the input graph itself as the construction graph and analyze the use of a construction procedure that is similar to Broder's algorithm for choosing a spanning tree uniformly at random. After that, a more incremental construction procedure is analyzed. It turns out that this procedure is superior to the Broder-based algorithm and produces additionally in a constant number of iterations a minimum spanning tree if the influence of the heuristic information is large enough. © 2008 Springer Berlin Heidelberg.Frank Neumann and Carstin Wit

Ant Colony Optimization and the minimum spanning tree problem

Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization. Solutions for a given problem are constructed by a random walk on a so-called construction graph. This random walk can be influenced by heuristic information about the problem. In contrast to many successful applications, the theoretical foundation of this kind of metaheuristic is rather weak. Theoretical investigations with respect to the runtime behavior of ACO algorithms have been started only recently for the optimization of pseudo-Boolean functions. We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem. In our investigations, we consider the minimum spanning tree (MST) problem and examine the effect of two construction graphs with respect to the runtime behavior. The choice of the construction graph in an ACO algorithm seems to be crucial for the success of such an algorithm. First, we take the input graph itself as the construction graph and analyze the use of a construction procedure that is similar to Broder's algorithm for choosing a spanning tree uniformly at random. After that, a more incremental construction procedure is analyzed. It turns out that this procedure is superior to the Broder-based algorithm and produces additionally in a constant number of iterations an MST, if the influence of the heuristic information is large enough. © 2010 Elsevier B.V. All rights reserved.Frank Neumann and Carsten Witthttp://www.elsevier.com/wps/find/journaldescription.cws_home/505625/description#descriptio