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论文作者:www.51lunwen.org论文属性:课程作业 Coursework登出时间:2016-01-03编辑:zhaotianyun点击率:16948
论文字数:3980论文编号:org201512282037073007语种:英语 English地区:澳门价格:免费论文
摘要:本文主要讲述了共通启发式演算法作为一种优化设计需要各领域多方面的很多搜索。
In order to locate the global optimum of a search problem accurately and efficiently under limited computational budget, a good balance between exploration and exploitation in the MA must be appropriately maintained throughout the optimization search process. Over the recent years, many dedicated MAs have been crafted to solve domain-specific problems more efficiently (Gwee & Lim, 2005; Lim, Li & Cao, 2005) while a distinct group of researchers has concentrated on the algorithmic aspect of MA as combinations of EAs with individual learning procedures (Tang, Lim & Ong, 2006, 2007). From a survey of the field, it is now well established that potential algorithmic improvements can be achieved by considering some important issues of MA such as the choice of individual learning procedure or local improvement procedure or meme to employ (Ong, Lim, Zhu & Wong, 2006), the frequency and intensity at which individual learning is used (Hart, 1994) including the subset of solutions on which individual learning is applied.
1.5.2 混合共通式启发法——1.5.2 Hybrid Metaheuristics
In recent years it has become evident that the concentration on a sole metaheuristic is rather restrictive. A skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility when dealing with real-world and large-scale problems. With the exception of memetic algorithms, metaheuristics such as genetic algorithm can be considered pure in the sense that they are not a combination of two or more approaches. When applying metaheuristics to solve an optimization problem, one way to pursue success is to adapt the technique using knowledge from the problem domain. This adaptation can be achieved by modifying its components and/or tuning its parameters.
Another approach that is commonly adopted is to combine two or more algorithms to develop a hybrid approach better suited for the given problem. Hybrid metaheuristics have proven to be successful in many optimisation problems and particularly in practical or real-world problems. It is not within the scope of this thesis to provide an extensive survey on hybrid metaheuristics. Instead, the reader is referred to some of the surveys and compilations of metaheuristics applications available in the literature (Aarts & Lenstra, 1997; Glover & Kochenberger, 2003; Osman & Kelly, 1996; Osman & Laporte, 1996; Rayward, Osman, Reeves & Smith, 1996; Reeves, 1995; Voss, Martello, Osman & Roucairol, 1999).
The hybridisation of metaheuristics has been proposed at various levels and in different ways. For example, the components of one metaheuristic can be embedded into another (using tabu lists within a genetic algorithm) or one metaheuristic can be used as a component to enhance the performance of another (simulated annealing as the local search phase in variable neighborhood search). The many ways in which metaheuristics can be combined makes it very difficult to describe or list all of them. Instead, it is perhaps more effective to differentiate between the designing principles used. In order to achieve this, it would be useful to have a nomenclature or framework that covers and permits the description of the majority of the hybrids proposed in the literature. At pr本论文由英语论文网提供整理,提供论文代写,英语论文代写,代写论文,代写英语论文,代写留学生论文,代写英文论文,留学生论文代写相关核心关键词搜索。