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Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms.

Zhang W, Bai E, He H, Cheng AM - Sensors (Basel) (2015)

Bottom Line: Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality.Convergence acceleration significantly reduces the search time.Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. wzzhang@hit.edu.cn.

ABSTRACT
Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.

No MeSH data available.


Related in: MedlinePlus

The influence of λ on (a) average time and (b) feasible solution number.
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sensors-15-13778-f007: The influence of λ on (a) average time and (b) feasible solution number.

Mentions: In Figure 7, we fix the population size as 200, the sub-population size as 20 and the sub-population iteration number as 10. The influence of λ is varied from 0.2, 0.4 to 0.6. The average time of λ = 0.4 is shorter than the others and the feasible solution numbers of λ = 0.4 are much higher than the others. Thus, we determine the λ = 0.4 for the C-SFLA.


Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms.

Zhang W, Bai E, He H, Cheng AM - Sensors (Basel) (2015)

The influence of λ on (a) average time and (b) feasible solution number.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507628&req=5

sensors-15-13778-f007: The influence of λ on (a) average time and (b) feasible solution number.
Mentions: In Figure 7, we fix the population size as 200, the sub-population size as 20 and the sub-population iteration number as 10. The influence of λ is varied from 0.2, 0.4 to 0.6. The average time of λ = 0.4 is shorter than the others and the feasible solution numbers of λ = 0.4 are much higher than the others. Thus, we determine the λ = 0.4 for the C-SFLA.

Bottom Line: Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality.Convergence acceleration significantly reduces the search time.Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. wzzhang@hit.edu.cn.

ABSTRACT
Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.

No MeSH data available.


Related in: MedlinePlus