Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test

A Computational Field Framework for Collaborative Task Execution in Volunteer Clouds

The increasing diffusion of cloud technologies is opening new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing their computational resources. The high dynamism and unpredictability of such scenarios call for decentralized self-* approaches to guarantee QoS. We present a simulation framework for collaborative task execution in volunteer clouds and propose one concrete instance based on Ant Colony Optimization, which is validated through a set of simulation experiments based on Google workload data

Parallel ACO with a Ring Neighborhood for Dynamic TSP

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc

Big data clustering using grid computing and ant-based algorithm

Big data has the power to dramatically change the way institutes and organizations use their data. Transforming the massive amounts of data into knowledge will leverage the organizations performance to the maximum.Scientific and business organizations would benefit from utilizing big data. However, there are many challenges in dealing with big data such as storage, transfer, management and manipulation of big data.Many techniques are required to explore the hidden pattern inside the big data which have limitations in terms of hardware and software implementation. This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm

ant-CBIR: a new method for radial furrow extraction in iris biometric

Iris recognition has evolved from first to second generation of biometric systems which capable of recognizing unique iris features such as crypts, collarette and pigment blotches. However, there are still ongoing researches on finding the best way to search unique iris features since iris image contains high noise. The high noise iris images (noisy iris); usually give the biometric systems to deliver erroneous results, leading to categorizations where the actual user is labeled as an impostor. Therefore, this study focuses on a novel method, targeted at overcoming the aforementioned challenge. We present the use of ant colony based image retrieval (ant–CBIR) technique as a successful method in recognizing the radial furrow in noisy iris. This method simulates the behavior of artificial ants, searching for pixel values of radial furrow based on an optimum pixel range. The evaluation of accuracy performance with and without the ant-CBIR application is measured using GAR parameter on UBIRIS.v1. Results show that the GAR is 79.9% with ant-CBIR implementation. The implication of this study contributes to a new feature extraction that has the ability of human-aided computing. Moreover, ant-CBIR helps to provide cost effective, easy maintenance and exploration of a long term data collection

Load Balancing in Computational Grids Using Ant Colony Optimization Algorithm

Grid computing is the combination of computer resources from multiple administrative domains for a common goal. Load balancing is one of the critical issues that must be considered in managing a grid computing environment. It is complicated due to the distributed and heterogeneous nature of the resources. An Ant Colony Optimization algorithm for load balancing in grid computing is proposed which will determine the best resource to be allocated to the jobs, based on resource capacity and at the same time balance the load of entire resources on grid. The main objective is to achieve high throughput and thus increase the performance in grid environment

Virtual Machine Optimization Scheduling Analysis Based on Cloud Computing in High-Performance Computing Platforms

The rational allocation of virtual machine resources in cloud computing is a critical technical issue, and the efficient utilization of virtual machine resources is a key factor influencing cloud service performance. Based on this, this paper proposes a novel ant colony optimization method that can effectively address the resource scheduling problem in cloud platforms