Meihong Wang

A Comparison of Four Popular Heuristics for Task Scheduling Problem in Computational Grid

The grid computational environment is very suit to meet the computational demands of large, diverse groups of tasks. And the task scheduling problem in it has been a research hotspot in recent years. Some heuristic methods have been used to optimize it and have got some good results. However, selecting the best one to use in a given environment remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each algorithm. Some comparisons have been made to them, but some new algorithms are not included in the comparisons. So, in this paper, four popular researched algorithms recently are selected, implemented, and analyzed. The four heuristics are Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization Algorithm and Simulated Annealing Algorithm. The evaluations include the schedule creating time, the makespan and the mean response time. It shows that for the cases studied here, the PSO heuristic performs better in comparison to the other techniques.

A hybrid recommendation algorithm based on Hadoop

Recommender system has been widely used and collaborative filtering algorithm is the most widely used algorithm in recommender system. As scale of recommender system continues to expand, the number of users and items of recommender system is growing exponentially. As a result, the single-node machine implementing these algorithms is time-consuming and unable to meet the computing needs of large data sets. To improve the performance, we proposed a distributed collaborative filtering recommendation algorithm combining k-means and slope one on Hadoop. Apache Hadoop is an open-source organizations distributed computing framework. In this paper, the former hybrid recommendation algorithm was designed to parallel on MapReduce framework. The experiments were applied to the MovieLens dataset to exploit the benefits of our parallel algorithm. The experimental results present that our algorithm improves the performance.

Grid Task Scheduling Based on Advanced No Velocity PSO

In computational grid environment, the task scheduling problem can be stated as finding a schedule scheme for a series of tasks to be executed on multiple resources, so that the task completion time can be minimized. There have been a lot of researches on scheduling algorithm, and heuristic approach have played very good role. For example, Genetic Algorithms, Simulated Annealing Algorithm, Ant Colony Optimization Algorithm and Particle Swarm Optimization Algorithm all have been applied to the scheduling problem. Particle Swarm Optimization algorithm has been shown good performance in many areas. Some experiments showed that it is better than Genetic Algorithms. In this paper, an efficient task scheduling method based on an advanced no velocity Particle Swarm Optimization is proposed. Simulation results in comparing the advanced no velocity Particle Swarm Optimization method and Ant Colony Optimization Algorithm are presented.

A Parallel Bee Colony Algorithm for Resource Allocation Application in Cloud Computing Environment

Cloud computing has been widely used in every social field. The problem of energy consumption in a cloud computing environment has brought cost pressure to service providers and affected the natural environment. However, the reasonable and efficient scheduling of resources could save a lot of energy for cluster. Meanwhile, its necessary for us to take into account of the emergent needs of every consumer. So the resource scheduling is often regarded as a multi-objective problem with the optimization of energy consumption and time cost. We redefine the problem in this paper and set up a multi-objective optimization model, and the parallel computing is improved on the basis of bee colony algorithm. Furthermore, multi-objective problem optimization based on fast non-dominated sorting method is used in parallel environment. Experimental results show that the proposed algorithm can save energy, reduce the execution time of tasks and have very good stability in parallel environment.

Research on Combined Rough Sets with Fuzzy Sets

Fuzzy set theory and rough set theory are useful mathematical tools for dealing with complex information in many real-world applications. In this paper we describe three aspects of this field: theoretical research into the properties of fuzzy sets and rough sets, research on the efficient implementation of this theory(attribute reduction, rule generation), and finally the development of hybrid systems that combine fuzzy sets or rough sets with other soft computing techniques such as neural networks and genetic algorithms. Hybrid algorithms can greatly improve the quality of the reconstructed system, bringing a much simpler and better solution to many practical applications.

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing

BACKGROUND Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). METHODS New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. RESULTS Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. CONCLUSIONS MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

WITHDRAWN: Automatic epileptic seizure detection in EEGs based on MF-DFA and SVM

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A novel long-term air quality forecasting algorithm based on kNN and NARX

In this paper, we propose a novel approach of forecasting long-term air quality. The methodology of our solution employs similar sequence search (using kNN) of historical data as reference to make up for the lack of information of the unknowing future conditions. Then these reference values as well as time series features, meterological data, air pollutant concentrations, neighbor stations air quality and weather forecast are utilized to forecast target stations future air quality (using NARX). We Compared with baseline approaches, and our approach shows superior performance.

Research of Advanced GTM and Its Application to Gas-Oil Reservoir Identification

Identification of gas-oil reservoir is always important but rather difficult in global gas-oil exploration. It is of the great significance to improve the accuracy of reservoir recognition. Seismic exploration is one of the most valuable methods of gas-oil exploration, and the huge amounts of seismic attribute data can be useful for gas-oil exploration. One limitation of the Generative Topographic Mapping (GTM) algorithm is that it cannot determine the classifications of the data points with close probabilities accurately, and it would be more likely to result in confused clarification and fuzzy boundary. To overcome the limitation, an advanced GTM algorithm with Euclidean Distance (GTM-ED) is proposed in this paper, and we use Euclidean Distance to compute the distance from the edge points to the neighbor centroids, and classify it to the closet class to avoid the problems of confused classification. And then the GTM-ED algorithm is used in the research of reservoir identification model, experiments are ...

The design of evolutionary multiple classifier system for the classification of microarray data

Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. In detail, we construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. Then we construct an ensemble system with the individuals in last generation in two ways: using the nondominated individuals; using all the individuals accompanied with a classifier selection process based on another GA. We test the two proposed ensemble methods based on two microarray data sets, and the experimental results show that these two methods are robust and can lead to promising results.