Essays and Surveys in Metaheuristics
Essays and Surveys in Metaheuristics
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DOI：
10.1007/978-1-4615-1507-4
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
C. Ribeiro;P. Hansen
中科院分区：
文献类型：
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作者：
C. Ribeiro;P. Hansen
蚁群优化算法（ACO）是一类基于标准构造性启发式算法和先前构造的解的信息构造解的构造性元启发式算法。本教程由三部分组成。第一个框架的ACO方法在当前的研究趋势的元启发式算法的组合优化;第二部分概述了在蚁群算法框架内的当前研究，报告了在不同问题上获得的最新结果，而第三部分集中在一个特定的研究路线，蚂蚁元启发式，提供了一些细节的算法，并提出最近获得的二次和频率分配问题的结果。
Ant Colony Optimization (ACO) is a class of constructive meta-heuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The first one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework, reporting recent results obtained on different problems, while the third part focuses on a particular research line, the ANTS metaheuristic, providing some details on the algorithm and presenting results recently obtained on the quadratic and on the frequency assignment problems.