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

页码：611 / +

页数：3

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