A survey on metaheuristics for stochastic combinatorial optimization
A survey on metaheuristics for stochastic combinatorial optimization
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DOI：
10.1007/s11047-008-9098-4
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发表时间：
2009-06-01
影响因子：
2.1
通讯作者：
Gutjahr, Walter J.
中科院分区：
文献类型：
--
作者：
Bianchi, Leonora;Dorigo, Marco;Gutjahr, Walter J.
元算法是一种通用的算法框架，通常是受自然启发的，旨在解决复杂的优化问题，几十年来，它们是一个不断增长的研究领域。近年来，元分析作为更经典的方法的成功替代品正在出现，也用于解决在其数学公式中包括不确定性，随机性和动态信息的优化问题。本文介绍了蚁群优化、进化计算、模拟退火、禁忌搜索等元算法，并对它们在随机组合优化问题中的应用进行了综述。共同的所有元分析，开放的问题，和可能的研究方向的问题提出和讨论。在本调查中，读者熟悉的元分析也发现指针经典算法的方法来优化下的不确定性，和有用的信息，开始工作在这个问题域，而读者新的元分析应该找到一个很好的教程，在这些元分析，目前正在应用于优化下的不确定性，和动机感兴趣的这一领域。
Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.