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17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)
Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization
Hong Kong, China
November 14-November 16
ISBN: 0-7695-2488-5
ASCII Text
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Dingjun Chen, Chung-Yeol Lee, Cheol Hoon Park, "Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization," Tools with Artificial Intelligence, IEEE International Conference on, pp. 126-133, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICTAI.2005.72,
author = {Dingjun Chen and Chung-Yeol Lee and Cheol Hoon Park},
title = {Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2005},
issn = {1082-3409},
pages = {126-133},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2005.72},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization
SN - 1082-3409
SP126
EP133
A1 - Dingjun Chen,
A1 - Chung-Yeol Lee,
A1 - Cheol Hoon Park,
PY - 2005
KW - null
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
Dingjun Chen, Korean Advanced Institute of Science and Technology
Chung-Yeol Lee, Korean Advanced Institute of Science and Technology
Cheol Hoon Park, Korean Advanced Institute of Science and Technology
We have implemented the sequential HGASA on a Sun Workstation machine; its performance seems to be very good in finding the global optimum of a sample function optimization problem as compared with some sequential optimization algorithms that offer low efficiency and limited reliability. However, the sequential HGASA generally needs a long run time cost. So we implemented a parallel HGASA using Message Passing Interface (MPI) on a high performance computer and performed many tests using a set of frequently used function optimization problems. The performance analysis of this parallel approach has been done on IBM Beowulf PCs Cluster in terms of program execution time, relative speed up and efficiency.
Citation:
Dingjun Chen, Chung-Yeol Lee, Cheol Hoon Park, "Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization," ictai, pp.126-133, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005
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