A DEA-GA multi-objective scheduling algorithm for Chip-Multiprocessor

DOI

10.2991/icssr-13.2013.153How to use a DOI?

Keywords

DEA; efficiency; Genetic Algorithm; multi-objective; scheduling; Chip Multiprocessor.

Abstract

In this paper, a Data Envelopment analysis based Genetic Algorithm (DEA-GA) is proposed for multi-objective scheduling on Chip-Multiprocessor. The proposal adopts modified GA as the searching heuristic to explore the solution space, and the fitness of each individual (schedule) is evaluated using the DEA approach. Three of the schedule metrics, namely makespan, energy and load balance are used to construct the multi-input multi-output Decision Making Units in the DEA, and the BCC super efficiency of each schedule is calculated. In the modified genetic algorithm, the metapopulation is divided into three subpopulations each optimizing a single metric. The top performance individuals in each subpopulation are then regrouped and applied DEA evaluation. Comparing to other multi-objective scheduling algorithm in simulations, our proposal always produces more efficient schedule solutions.

Copyright

© 2013, 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/).

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TY  - CONF
AU  - Song Chai
AU  - Yubai Li
AU  - Chang Wu
AU  - Jian Wang
PY  - 2013/07
DA  - 2013/07
TI  - A DEA-GA multi-objective scheduling algorithm for Chip-Multiprocessor
BT  - Proceedings of the 2nd International Conference on Science and Social Research (ICSSR 2013)
PB  - Atlantis Press
SP  - 661
EP  - 665
SN  - 1951-6851
UR  - https://doi.org/10.2991/icssr-13.2013.153
DO  - 10.2991/icssr-13.2013.153
ID  - Chai2013/07
ER  -