Computation model and improved ACO algorithm for p//T

被引：0

作者：

Yi Yang & Lai Jieling School of Information Science & Technology

机构：

来源：

JournalofSystemsEngineeringandElectronics | 2009年 / 20卷 / 06期

基金：

中国国家自然科学基金;

关键词：

scheduling; evolutionary computation; ant colony optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Scheduling jobs on parallel machines to minimize the total tardiness(p//T) is proved to be NP hard.A new ant colony algorithm to deal with p//T(p//T ACO) is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed.Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved.The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.

引用

页码：1336 / 1343

页数：8