Heuristic algorithms for I/O scheduling for efficient retrieval of large objects from tertiary storage

2002 ◽  
Author(s):  
ChanHo Moon ◽  
Hyunchul Kang
Keyword(s):  
Heuristic Algorithms ◽  
Tertiary Storage ◽  
Efficient Retrieval

scholarly journals Efficient Retrieval Of Html Documents Using Hybrid Meta-Heuristic Approaches In Web Document Clustering

2019 ◽  
Vol 8 (5C) ◽  
pp. 1350-1354
Keyword(s):  
Web Search ◽  
Heuristic Algorithms ◽  
Document Clustering ◽  
Web Documents ◽  
Clustering Problem ◽  
Web Document ◽  
Efficient Retrieval ◽  
Web Document Clustering ◽  
The Given ◽  
The Web

With the rapid growth of web documents on WWW, it is becoming difficult to organize, analyze and present these documents efficiently. Web search engines return many documents to the web user, out of which some are relevant and some irrelevant documents to the topic, for the given query. Web search is usually performed using only features extracted from the web page text. HTML tags with particular meanings have been found to improve the efficiency of the information retrieval System. However, organizing documents in a way that will improve search without additional cost or complexity is still a great challenge. Clustering can play an important role to organize such a large number of documents into several groups. However due to limitations in existing techniques of clustering, scientists have begun using Meta-heuristic algorithms for the clustering problem of documents. In this paper, we presented a document clustering method that uses HTML tags and Metaheuristic approaches. The hybrid PSO+ACO+K-means algorithm is used for clustering the documents. In the proposed approach, results are analyzed on WEBKB dataset

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Segmentation of Brain Tumors using Meta Heuristic Algorithms

2014 ◽  
Vol 2014 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Jobin Christ ◽  
◽  
S. Sivagowri ◽  
Ganesh Babu ◽  
◽  
...  
Keyword(s):  
Brain Tumors ◽  
Heuristic Algorithms

Two Enhanced Heuristic Algorithms for the Minimum Initial Marking Problem of Petri Nets

2009 ◽  
Vol E92-A (11) ◽  
pp. 2732-2744
Author(s):  
Satoru OCHIIWA ◽  
Satoshi TAOKA ◽  
Masahiro YAMAUCHI ◽  
Toshimasa WATANABE
Keyword(s):  
Petri Nets ◽  
Heuristic Algorithms

Two Heuristic Algorithms for the Minimum Initial Marking Problem of Timed Petri Nets

2013 ◽  
Vol E96.A (2) ◽  
pp. 540-553
Author(s):  
Satoru OCHIIWA ◽  
Satoshi TAOKA ◽  
Masahiro YAMAUCHI ◽  
Toshimasa WATANABE
Keyword(s):  
Petri Nets ◽  
Heuristic Algorithms ◽  
Timed Petri Nets

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

Investigation of the diversity index trend of the Search Group Algorithm in comparison with others meta-heuristic algorithms

2017 ◽  
Author(s):  
Rafael Souza ◽  
Leandro Fadel Miguel ◽  
Matheus Silva Gonçalves ◽  
Rafael Holdorf Lopez
Keyword(s):  
Heuristic Algorithms ◽  
Diversity Index
Sign in / Sign up

Export Citation Format

Share Document