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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Huang Guangdong, Ling Ping, Wang Qun, "A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 3, pp. 611-616, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.402,
author = {Huang Guangdong and Ling Ping and Wang Qun},
title = {A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {3},
year = {2007},
isbn = {0-7695-2909-7},
pages = {611-616},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.402},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling
SN - 0-7695-2909-7
SP611
EP616
A1 - Huang Guangdong,
A1 - Ling Ping,
A1 - Wang Qun,
PY - 2007
KW - null
VL - 3
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Huang Guangdong, China University of Geosciences, China
Ling Ping, Beihang University, China
Wang Qun, China University of Geosciences, China
This paper presents a hybrid metaheuristic ACOGA for the problem of sports competition scheduling (SCS). ACO-GA combines ant colony optimization (ACO) and genetic algorithms (GA). The procedures of ACO-GA are as follows. First, GA searches the solution space and generates activity lists to provide the initial population for ACO. Next, ACO is executed, when ACO terminates, the crossover and mutation operations of GA generate new population. ACO and GA search alternately and cooperatively in the solution space. Then we test ACO-GA with Oliver30 and att48. The results indicate that ACO-GA is an effective method. Finally this paper deals with SCS using ACO-GA.
Citation:
Huang Guangdong, Ling Ping, Wang Qun, "A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling," snpd, vol. 3, pp.611-616, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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