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无数的搜索和优化技术 [3]

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:16832

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

关键词:优化设计共通启发式演算法Metaheuristic

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。

optimization problems namely the high-level synthesis, multiprocessor scheduling, hybrid flow shop scheduling and flexible manufacturing systems.


1.3 回顾共通启发式演算法——1.3 Review of Metaheuristic Approaches

1.3.1 介绍——1.3.1 Introduction

In recent years, optimization algorithms have received increasing attention by the research community as well as the industry. Scientifically, the field of optimization algorithms is a highly relevant research area, because these algorithms can find approximate solutions to NP-hard problems and solutions to problems where no analytic method exists, e.g. for solving non-linear differential equations. Optimization algorithms have a very broad range of application, since many problems in science and industry can be formulated as an optimization task where the objective is to minimize or maximize a given objective function f. In other words, to find a solution s in the search space S of possible solutions such that f(s) is minimized (or maximized).

The manual search of a solution with a slight improvement is often tedious, if not impossible, because manual optimization requires a great deal of insight and patience. Furthermore, manual optimization often limits the scope of the search process to what the human expert is trained to consider as a good solution. Conversely, optimization algorithms automate the search and are not biased in scope regarding the solutions. The wide range of real-world optimization problems and the importance of finding good approximate solutions have lead to a great variety of optimization techniques (for a comprehensive survey, see (Michalewicz & Fogel, 2000). In this context, metaheuristic algorithms (Blum & Roli, 2003; Glover & Kochenberger, 2003; Hoos & Tzle) are a particularly promising approach, because this technique has shown good and robust performance on a broad range of real-world problems. This section presents a brief overview of metaheuristics with the aim to provide a consistent theoretical background on the field of metaheuristics for combinatorial optimization to underpin the rest of this thesis. A review on the main concepts and terminology is presented. The algorithms description of the different metaheuristics and their hybrids employed in this thesis will be presented in the coming chapters.

Metaheuristic algorithms belong to the class of approximation search algorithms. These are powerful and efficient tools to solve optimization problems. They are formally defined as iterative generation processes which guide a subordinate heuristic by combining intelligently different concepts for exploring and exploiting the search space. Learning strategies are used to structure information in order to find efficiently near-optimal solutions (Osman & Laporte, 1996). They differ from traditional heuristic search (Russell & Norvig, 2003) in that a set of high level strategies are usually integrated into the search framework in order to explore the solution space both effectively and efficiently. In contrast to many classical methods, metaheuristics do not build a model of the tackled optimization problem, but treat the problem as it is (black-box optimization). Therefore, they are directly applicable to complex real-world problems with relatively few modifications.

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