Dynamic ant colony optimisation

被引：21

作者：

Angus, D ^{[1
]}

Hendtlass, T ^{[1
]}

机构：

[1] Swinburne Univ Technol, Ctr Intelligent Syst & Complex Proc, Hawthorn, Vic 3122, Australia

来源：

APPLIED INTELLIGENCE | 2005年 / 23卷 / 01期

关键词：

meta-heuristics; optimisation; Ant Colony optimisation;

D O I：

10.1007/s10489-005-2370-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time. However in the real world changing circumstances may mean that a previously optimum solution becomes suboptimal. This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution for one set of circumstances to the optimal solution for another set of circumstances. Results are given for a preliminary investigation based on the classical travelling salesman problem. It is concluded that, for this problem at least, the time taken for the solution adaption process is far shorter than the time taken to find the second optimum solution if the whole process is started over from scratch.

引用

页码：33 / 38

页数：6

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