Strategies for accelerating Ant Colony Optimization algorithms on Graphical Processing Units

被引：18

作者：

Catala, Alejandro ^{[1
]}

Jaen, Javier ^{[1
]}

Mocholi, Jose A. ^{[1
]}

机构：

[1] Univ Politecn Valencia, Dept Informat Syst, Cami de Vera S-N, Valencia 46022, Spain

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/CEC.2007.4424511

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

Ant Colony Optimization (ACO) is being used to solve many combinatorial problems. However, existing implementations fail to solve large instances of problems effectively. In this paper we propose two ACO implementations that use Graphical Processing Units to support the needed computation. We also provide experimental results by solving several instances of the well-known Orienteering Problem to show their features, emphasizing the good properties that make these implementations extremely competitive versus parallel approaches.

引用

页码：492 / +

页数：2

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