Fast ant colony optimization on runtime reconfigurable processor arrays.pdf

下载文档

3
0
约4.28万字
约 17页
2017-04-06 发布于江苏
举报
版权申诉
保障服务

Fast ant colony optimization on runtime reconfigurable processor arrays.pdf

1、本文档共17页，可阅读全部内容。
2、有哪些信誉好的足球投注网站（book118）网站文档一经付费（服务费），不意味着购买了该文档的版权，仅供个人/单位学习、研究之用，不得用于商业用途，未经授权，严禁复制、发行、汇编、翻译或者网络传播等，侵权必究。
3、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。如您付费，意味着您自己接受本站规则且自行承担风险，本站不退款、不进行额外附加服务；查看《如何避免下载的几个坑》。如果您已付费下载过本站文档，您可以点击这里二次下载。
4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等，请点击“版权申诉”（推荐），也可以打举报电话：400-050-0827(电话支持时间：9:00-18:30)。

Fast ant colony optimization on runtime reconfigurable processor arrays

Genetic Programming and Evolvable Machines, 3, 345–361, 2002 ? 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays DANIEL MERKLE AND merkle,middendorf@informatik.uni-leipzig.de MARTIN MIDDENDORF Parallel Computing and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, Ostenstr. 26-28, D-04109, Leipzig, Germany Submitted August 10, 2001; Revised April 1, 2002 Abstract. Ant Colony Optimization (ACO) is a metaheuristic used to solve combinatorial optimization problems. As with other metaheuristics, like evolutionary methods, ACO algorithms often show good optimization behavior but are slow when compared to classical heuristics. Hence, there is a need to find fast implementations for ACO algorithms. In order to allow a fast parallel implementation, we propose several changes to a standard form of ACO algorithms. The main new features are the non- generational approach and the use of a threshold based decision function for the ants. We show that the new algorithm has a good optimization behavior and also allows a fast implementation on reconfigurable processor arrays. This is the first implementation of the ACO approach on a reconfigurable architecture. The running time of the algorithm is quasi-linear in the problem size n and the number of ants on a reconfigurable mesh with n2 processors, each provided with only a constant number of memory words. Keywords: ACO, reconfigurable architectures, quadratic assignment 1. Introduction Ant Colony Optimization (ACO) is a metaheuristic that has been applied success- fully to solve various combinatorial optimization problems (for an overview see [6]). In ACO, several generations of artificial ants search for good solutions. Every ant of a generation builds up a solution step by step, thereby going through several deci- sions until a solution is found. Similar to real ants, the artificial ants that found a