Enhancement of coarse-grained parallel genetic algorithm for shortest path routing

2013 ◽  
Author(s):  
L. Venkatesan ◽  
P. Sivakumar
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Coarse Grained ◽  
Parallel Genetic Algorithm ◽  
Shortest Path Routing

scholarly journals Parallel Genetic Algorithm for Shortest Path Routing Problem with Collaborative Neighbors

2015 ◽  
Vol 37 ◽  
pp. 327
Author(s):  
Reza Roshani ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Shortest Path ◽  
Migration Rate ◽  
Mesh Network ◽  
Alternative Methods ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Fine Grained

Shortest path routing is generally known as a kind of routing widely availed in computer networks nowadays. Although advantageous algorithms exist for finding the shortest path, however alternative methods may have their own supremacy. In this paper, parallel genetic algorithm for finding the shortest path routing is resorted to. In order to improve the computation time in this routing algorithm and to distribute the load balance between the processors as well, Fine-Grained parallel GA model is opted for. The proposed algorithm was simulated on Wraparound Mesh network topologies in different sizes. To this end, several experiments were anchored to identify the most influential parameters such as Migration rate, Mutation rate, and Crossover rate. The simulation result shows that best result of mutation rate is: about 0.02 and 0.03, and migration rate for transmission to the neighbor’s node is 3 of the best chromosomes. This study has already shown that through using performance-based GA which uses fine-grained parallel algorithms, timing germane shortest path routing can be improved.

scholarly journals The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

2015 ◽  
Vol 10 (8) ◽  
pp. 11
Author(s):  
Liping Wu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Coarse Grained ◽  
Map Reduce ◽  
Parallel Genetic Algorithm ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

scholarly journals A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Ningjiu Tang ◽  
Xin Xia
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Coarse Grained ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Parallel Genetic Algorithm ◽  
Support Vectors ◽  
Optimal Feature Subset ◽  
Optimal Feature

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

Sign in / Sign up

Export Citation Format

Share Document