Lévy mutation in artificial bee colony algorithm for gasoline price prediction

In this paper, a mutation strategy that is based on Lévy Probabily Distribution is introduced in Artificial Bee Colony algorithm. The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price

A near-optimal centroids initialization in K-means algorithm using bees algorithm

The K-mean algorithm is one of the popular clustering techniques.The algorithm requires user to state and initialize centroid values of each group in advance. This creates problem for novice users especially to those who have no or little knowledge on the data.Trial-error attempt might be one of the possible preference to deal with this issue.In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set.Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets

Relationship based replication algorithm for data grid

Data Grid is an infrastructure that manages huge amount of data files and provides intensive computational resources across geographically distributed systems.To increase resource availability and to ease resource sharing in such environment, there is a need for replication services.This research proposes a replication algorithm, termed as Relationship based Replication (RBR) that integrates users, grid and system perspective.In particular, the RBR includes information of three different relationships in identifying file(s) that requires replication; file-to-user, file-to-file and file-to-grid. Such an approach overcomes existing algorithms that is based either on users request or resource capabilities as an individual. The Relationship based Replication algorithm aims to improve the Data Grid performance by reducing the job execution time, bandwidth and storage usage.The RBR was realized using a network simulation (OptorSim) and experiment results revealed that it offers better performance than existing replication algorithms

Replication in data grid: Determining important resources

Replication is an important activity in determining the availability of resources in data grid.Nevertheless, due to high computational and storage cost, having replicas for all existing resources may not be an efficient practice. Existing approach in data replication have been focusing on utilizing information on the resource itself or network capability in order to determine replication of resources.In this paper, we present the integration of three types of relationships for the mentioned purpose. The undertaken approach combines the viewpoint of user, file system and the grid itself in identifying important resource that requires replication.Experimental work has been done via OptorSim and evaluation is made based on the job execution time.Results suggested that the proposed strategy produces a better outcome compared to existing approaches

A dynamic replication strategy based on exponential growth/decay rate

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration.To increase resource availability and to ease resource sharing in such environment, there is a need for replication services.Data replication is one of the methods used to improve the performance of data access in distributed systems.In this paper, we include issues arising in data replication domain and also we propose a dynamic replication strategy that is based on exponential growth or decay rate. The purpose of the proposed strategy is to identify which files to be replicated.This is achieved by estimating number of accessed of a file in the upcoming time interval.The greater the value, the more popular the file is and therefore will be selected to be replicate

A dynamic replica creation: Which file to replicate?

Data Grid is an infrastructure that manages huge amount of data files and provides intensive computational resources across geographically distributed collaboration.To increase resource availability and to ease resource sharing in such environment, there is a need for replication services.Data replication is one of the methods used to improve the performance of data access in distributed systems.In this paper, we propose a dynamic replication strategy that is based on exponential growth or decay rate and dependency level of data files (EXPM).Simulation results (via Optorsim) show that EXPM outperformed LALW in the measured metrics – mean job execution time, effective network usage and average storage usage

Functional and structural descriptors for software component retrieval

Identifying appropriate software components in a repository is an important task in software reuse after all, components must be found before they can be reused. Program source code documents written in a computer programming language has the possibility to be a software component. Program source code is a form of data, containing both structure and function it is therefore important to make use of this information in representing programs in a software repository. Existing approaches in software component retrieval systems focus on retrieving a component based on either its function or structure. Such an approach may not be suitable to users that require examples of programs that illustrate a particular function and structure, there is therefore a need for combining this information together. The objective of this research is to build a software repository of Java programs, to facilitate the search and selection of programs using the information about a program's function and structure. The hypothesis is that retrieval of program source code is better undertaken using a combination of functional and structural descriptors rather than using functional descriptors on their own. This thesis presents a program retrieval and indexing model which can be used in developing a source code retrieval system. The model reveals on how functional and structural descriptors are identified and combined into a single representation. The functional descriptors are identified by extracting selected terms from program source code and a weighting scheme is adopted to differentiate the importance of terms. As programs in the repository are from open-source applications, extracting information that does not rely on semantic terms would be beneficial, as these programs are written by various developers with different programming background and experience. Structural descriptors that comprise of information generated based on structural relationships, such as design patterns and software metrics, are extracted from a program to be added as the program descriptor. The functional and structural descriptors are combined into a single index, known as a compound index, which is used as a program descriptor. The degree of similarity between a given query and programs in a repository is identified using measurements undertaken based on vector model and data distribution based approaches. Lessons learned from the experiments undertaken reveals that programs retrieved using the proposed method are less complex and easy to maintain. Furthermore, it is suggested that programs from different application domains contain different trends in their software metrics

Determining Multi-Criteria Supplier Selection towards Sustainable Development of IT Project Outsourcing

Due to competitiveness in the global business, many organizations have sought alternative to improve their businesses and operations by outsourcing projects and this includes Information Technology (IT) projects. Selecting the most suitable IT supplier is important to ensure sustainable development of the projects. Supplier is selected based on a set of criteria used in the decision process. The criteria can be comprised into tangible and intangible criteria. Many studies have attempted to determine the criteria to be used in selecting IT supplier, nevertheless, it has yet to be reported on a standardize set of criteria to be used in IT outsourcing projects. Thus outsourcing decisions are often made under uncertainty and incomplete information which leads to weak decision and high risk of projects failure. Therefore, the study aimed to determine multi-criteria for supplier selection in order to ensure the sustainable development of IT outsourcing projects. The criteria were identified using comprehensive review approach that utilizes searching information related to multi criteria supplier selection in IT outsourcing and successful criteria of IT outsourcing projects. As a result, the identified criteria is proposed as a standardize criteria in selecting supplier for IT outsourcing projects. Such a contribution is hoped to benefit businesses for various IT outsourcing projects

Document clustering based on firefly algorithm

Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFAII outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved