英语论文网

n to the important concepts covered in this thesis. First, the fundamental problems of design optimization are addressed. Next, meta-heuristic methods as powerful solution tools are discussed in general. Then, memetic algorithms are introduced, including its rationale and application areas. This is followed by the introduction of the concept of fitness landscapes including search space, the neighborhood relation, and the guiding function. Finally, the objectives and the organization of the thesis are laid out.

1.2 优化设计——1.2 Design Optimization

Optimization is a key topic in computer science, artificial intelligence, operational research, and related fields. Outside these scientific communities the real meaning of optimization is rather imprecise: it simply means “making better”. However, in the context of this thesis, optimization is the process of trying to find the best possible solution to an optimization problem within a given time limit. Design optimization is the issue of determining the set of design parameters that will optimize a given objective. It refers to problems that require searching for a best configuration of a set of variables to achieve certain goal. Design optimization is of interest to many design problems, especially complicated problems. For example, when scheduling manufacturing processes, one needs to decide when to use what manufacturing resources to have the resources collaborate in a reliable and efficient manner. Major difficulties in design optimization problems are: 1) the solution space is too large for exhaustive search; 2) the relationship between the design parameters and the optimization objective has not been completely understood, and hence the problem cannot be solved through analytical methods. Many optimization problems are fundamentally hard. This is the most typical scenario when it comes to realistic and relevant problems in industry or science. Essentially, a ‘hard' problem is one for which we cannot guarantee to find the best solution in a reasonable amount of time. Hard problems usually feature a high number of degrees of freedom, non-linearity, and the existence of large number of local optima (Chiarandini & Tzle, 2003; Garnier & Kallel, 2002; Hoos & Tzle, 2005). The intrinsic difficulty of these problems often leads to intractable amount of computational time for solving them. Hard combinatorial optimization problems pose challenges to search algorithms. The solution space usually contains a large number of local optimal points, and the computational cost for reaching a global optimum may be too high for practical use. Since most real world optimization problems seem to be both fundamentally hard and also practically hard, research into good approximate methods remains valuable, and continues worldwide. Any new development in optimization that leads to better results on a particular problem, or to new approximate methods which may be applied to a wide range of problems, is of considerable value to science and industry. It may lead to substantial financial savings for corporations, significantly more effective health care, or appreciably cheaper and more reliable communications. This collection of ongoing motivations has led to a fairly steady stream of ideas for optimization algorithms to solve hard optimization problems. In this work, we apply metaheuristic and memetic algorithms to solve four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。