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

Analyse the Performance of Mobile Peer to Peer Network using Ant Colony Optimization

A mobile peer-to-peer computer network is the one in which each computer in the network can act as a client or server for the other computers in the network. The communication process among the nodes in the mobile peer to peer network requires more no of messages. Due to this large number of messages passing, propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency of the mobile peer to peer network. The proposed method improves the data availability and consistency across the entire network and also reduces the data latency and the required number of message passes for any specific application in the network. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and increased availability, enhanced consistency and scalability of the network. The simulation results shows that reduces the number of message sent for any specific application and average delay and increases the packet delivery ratio in the network

An Efficient Ant Colony Optimization Framework for HPC Environments

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale).This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). JRB acknowledges funding from the Ministry of Science and Innovation of Spain MCIN / AEI / 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS), and from MCIN / AEI / 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). Authors also acknowledge the Galician Supercomputing Center (CESGA) for the access to its facilities. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/3

Automated, Parallel Optimization Algorithms for Stochastic Functions

The optimization algorithms for stochastic functions are desired specifically for real-world and simulation applications where results are obtained from sampling, and contain experimental error or random noise. We have developed a series of stochastic optimization algorithms based on the well-known classical down hill simplex algorithm. Our parallel implementation of these optimization algorithms, using a framework called MW, is based on a master-worker architecture where each worker runs a massively parallel program. This parallel implementation allows the sampling to proceed independently on many processors as demonstrated by scaling up to more than 100 vertices and 300 cores. This framework is highly suitable for clusters with an ever increasing number of cores per node. The new algorithms have been successfully applied to the reparameterization of a model for liquid water, achieving thermodynamic and structural results for liquid water that are better than a standard model used in molecular simulations, with the the advantage of a fully automated parameterization process

Automated Test Sequence Optimization Based on the Maze Algorithm and Ant Colony Algorithm

With the rapid development of China train operation and control system, validity and safety of behavioral functions of the system have attracted much attention in the railway domain. In this paper, an automated test sequence optimization method was presented from the system functional requirement specification of the high-speed railway. To overcome the local optimum of traditional ant colony algorithm, the maze algorithm is integrated with the ant colony algorithm to achieve the dynamical learning capacity and improve the adaptation capacity to the complex and changeable environment, and therefore, this algorithm can produce the optimal searching results. Several key railway operation scenarios are selected as the representative functional scenarios and Colored Petri Nets (CPN) is used to model the scenarios. After the CPN model is transformed into the extensible markup language (XML) model, the improved ant colony algorithm is employed to generate the optimal sequences. The shortest searching paths are found and the redundant test sequences are reduced based on the natural law of ants foraging. Finally, the Radio Blocking Center (RBC) test platform is designed and used to validate the optimal sequence. Testing results show that the proposed method is able to optimize the test sequences and improve the test efficiency successfully