Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search

被引：0
|
作者：
Neumann, Frank [1 ]
Sudholt, Dirk [2 ]
Witt, Carsten [2 ]
机构：
[1] Max Planck Inst Informat, Saarbrucken, Germany
[2] Tech Univ Dortmund, Informat 2, Dortmund, Germany
来源：
ANT COLONY OPTIMIZATION AND SWARM INTELLIGENCE, PROCEEDINGS | 2008年 / 5217卷
关键词：
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：
Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local search into an ACO algorithm enhances the optimization process significantly. On the other hand, we illustrate the drawback that such a combination might have by showing that this may prevent all ACO algorithm from obtaining optimal solutions.
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页码：132 / +
页数：2
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