A Hybrid Metaheuristic ACO-GA with an Application in Sports Competition Scheduling

Authors:
Huang Guangdong;Ling Ping;Wang Qun
Affiliations:
China University of Geosciences, China;Beihang University, China;China University of Geosciences, China
Venue:
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
Year:
2007

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IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning

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Abstract

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.