A hybrid metaheuristic ACO-GA with an application in sports competition scheduling

被引：9
|
作者：
Huang Guangdong [1 ]
Ping, Ling [2 ]
Qun, Wang [1 ]
机构：
[1] China Univ Geosci, Beijing 100083, Peoples R China
[2] Beihang Univ, Sch Econom & Management, Beijing 100083, Peoples R China
来源：
SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 3, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/SNPD.2007.402
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a hybrid metaheuristic ACO-GA for the problem of sports competition scheduling (SCS). ACO-GA combines ant colony optimization (A CO) 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 A CO. Next, A CO is executed, when ACO terminates, the crossover and mutation operations of GA generate new population. A CO 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.
引用
收藏
页码：611 / +
页数：3
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