Analysis of Two Phase Query Optimization Algorithm for Generating Optimal Query Plan using Randomized Algorithm

2020 ◽  
Author(s):  
Pramod Kumar Yadav ◽  
Sam Rizvi
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Randomized Algorithm ◽  
Two Phase ◽  
Query Plan

Multi-dimensional query optimization algorithm for bitmap index with binning

2010 ◽  
Vol 30 (8) ◽  
pp. 2013-2016 ◽  
Author(s):  
Li-ming WANG ◽  
Xiao CHENG ◽  
Yu-mei CHAI
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Bitmap Index

scholarly journals Research on classification query optimization algorithm in data stream

2015 ◽  
Author(s):  
Hong Zhou ◽  
Bin Wang ◽  
Chunyan Fu ◽  
Yuan Zhi ◽  
Jiamei Xue
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Data Stream

A PSO-Based Web Document Query Optimization Algorithm

2006 ◽  
pp. 609-615 ◽  
Author(s):  
Ziqiang Wang ◽  
Xin Li ◽  
Dexian Zhang ◽  
Feng Wu
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Web Document

A Two-Phase Meta Heuristic Approach to the Location-Routing Problem for Transportation Network Planning

2013 ◽  
Vol 361-363 ◽  
pp. 1900-1905 ◽  
Author(s):  
Ji Ung Sun
Keyword(s):  
Ant Colony Optimization ◽  
Facility Location ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Transportation Network ◽  
Ant Colony Optimization Algorithm ◽  
Two Phase ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing

In this paper we consider the location-routing problem which combines the facility location and the vehicle routing decisions. In this type of problem, we have to determine the location of facilities within a set of possible locations and routes of the vehicles to meet the demands of number of customers. Since the location-routing problem is NP-hard, it is difficult to obtain optimal solution within a reasonable computational time. Therefore, a two-phase ant colony optimization algorithm is developed which solves facility location problem and vehicle routing problem hierarchically. Its performance is examined through a comparative study. The experimental results show that the proposed ant colony optimization algorithm can be a viable solution method for the general transportation network planning.

scholarly journals A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ramalingam Gomathi ◽  
Dhandapani Sharmila
Keyword(s):  
Semantic Web ◽  
Query Optimization ◽  
Execution Time ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Web Data ◽  
Query Plan ◽  
Plan Generation ◽  
Description Framework ◽  
Rdf Graphs

The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

DE-PSO: A NEW HYBRID META-HEURISTIC FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

2011 ◽  
Vol 07 (03) ◽  
pp. 363-381 ◽  
Author(s):  
MILLIE PANT ◽  
RADHA THANGARAJ ◽  
AJITH ABRAHAM
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Test Functions ◽  
Global Optimization Algorithm ◽  
Two Phase ◽  
Swarm Optimization ◽  
Life Problems

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

Sign in / Sign up

Export Citation Format

Share Document