Based on Hybrid Particle Swarm Optimization Algorithm Respectively Research on Multiprocessor Task Scheduling

DOI

10.2991/isaeece-17.2017.63How to use a DOI?

Keywords

Multiprocessor, Task scheduling, TSP, HPSO.

Abstract

Multiprocessor system plays an important role in the computer, in order to improve the parallel computing performance of the system, its essence is to solve a multiprocessor system task scheduling algorithm of NP problem, and the TSP (traveling salesman problem) is a typical NP-complete problem. This article will be attributed to solve multiprocessor task scheduling multiprocessor task scheduling of TSP combination optimization problems. In this article, through the experiment to verify the hybrid particle swarm optimization algorithm and genetic algorithm in solving TSP multiprocessor task scheduling optimization problems, the experimental results show that the hybrid particle swarm optimization algorithm in solving the questions of different size of task scheduling is not only to solve the high quality, and solving the faster, the perform better than genetic algorithm.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Hui Tian
PY  - 2017/03
DA  - 2017/03
TI  - Based on Hybrid Particle Swarm Optimization Algorithm Respectively Research on Multiprocessor Task Scheduling
BT  - Proceedings of the 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017)
PB  - Atlantis Press
SP  - 330
EP  - 333
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-17.2017.63
DO  - 10.2991/isaeece-17.2017.63
ID  - Tian2017/03
ER  -