A DEA-GA multi-objective scheduling algorithm for Chip-Multiprocessor
Song Chai, Yubai Li, Chang Wu, Jian Wang
Available Online July 2013.
- https://doi.org/10.2991/icssr-13.2013.153How to use a DOI?
- DEA; efficiency; Genetic Algorithm; multi-objective; scheduling; Chip Multiprocessor.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/icssr-13.2013.153 ID - Chai2013/07 ER -