TY - JOUR
T1 - Energy efficiency based on high performance particle swarm optimization
T2 - A case study
AU - Liao, Ming Yi
AU - Tsai, Chun Wei
AU - Yang, Chu Sing
AU - Chiang, Ming Chao
AU - Lai, Chin Feng
N1 - Funding Information:
Acknowledgement The authors thank the editor and anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the National Science Council, Taiwan, R.O.C., under Contact Nos. NSC100-2218-E-041-001-MY2, NSC99-2219-E-006-001, and NSC100-2219-E-006-002.
PY - 2013/2
Y1 - 2013/2
N2 - Finding solutions to green manufacturing, green production, and increasing energy efficiency is definitely our responsibility to resist changing the vulnerable environment dramatically. Over the past, several practical techniques have been proposed to reduce the greenhouse gas emissions, e.g., increasing energy efficiency, reducing power usage, using sustainable energy, and recycling. This paper first gives a brief review of green computing and then presents a case study for energy efficiency called energy efficient particle swarm optimization (EEPSO). The proposed algorithm integrates particle swarm optimization and triangle inequality for improving energy efficiency of computers, by using the clustering results to adjust the CPU frequency of network management system. Simulation results show that not only can the proposed algorithm significantly reduce the computation time, but it can also be extended to enhance the performance of network traffic control system to further reduce the power they consume.
AB - Finding solutions to green manufacturing, green production, and increasing energy efficiency is definitely our responsibility to resist changing the vulnerable environment dramatically. Over the past, several practical techniques have been proposed to reduce the greenhouse gas emissions, e.g., increasing energy efficiency, reducing power usage, using sustainable energy, and recycling. This paper first gives a brief review of green computing and then presents a case study for energy efficiency called energy efficient particle swarm optimization (EEPSO). The proposed algorithm integrates particle swarm optimization and triangle inequality for improving energy efficiency of computers, by using the clustering results to adjust the CPU frequency of network management system. Simulation results show that not only can the proposed algorithm significantly reduce the computation time, but it can also be extended to enhance the performance of network traffic control system to further reduce the power they consume.
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U2 - 10.1007/s11235-011-9641-y
DO - 10.1007/s11235-011-9641-y
M3 - Article
AN - SCOPUS:84879605383
SN - 1018-4864
VL - 52
SP - 1293
EP - 1304
JO - Telecommunication Systems
JF - Telecommunication Systems
IS - 2
ER -
