华北电力大学毕业设计-苑曙光

华北电力大学毕业设计（论文）撰写格式：

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摘要

在最近二十年，作为一类新兴的优化技术，多目标进化算法吸引了极大关注，许多学者提出了不同的算法，多目标进化算法已经成为处理多目标工程设计和科学研究问题的重要方法。许多MOEA的方面被广泛地调研，然而一些问题仍然没有被很好地受到关注。例如，随着这类算法的快速发展，对算法之间性能进行比较变得越来越重要。本文分析总结了两种目前流行的所目标进化算法的基本原理，并通过算例来比较它们的性能。

本文主要工作内容如下：

1.简要回顾了多目标进化算法的发展历史，按照算法原理与进化模式将算

法分类。

2.简述多目标问题及进化算法的相关技术，详细分析了NSGA-II算法和

MOGLS算法。

3.分别利用NSGA-II算法和MOGLS算法对算例进行求解，并用C指标对

两种算法的结果进行评价，得出它们各自的优缺点。

多目标问题仍向算法设计，呈现和执行提出挑战。不断变化的多目标问题很少被考虑到它的时变特性，对此有效的多目标进化算法很罕见，多目标进化算法的结合量计算和有区别的进化还始终停留在初级阶段。多目标进化算法的应用应该在未来不断地延续，MOEA的理论分析比它本身更复杂而且应该通过主要从事计算机和数学研究人员的努力工作来解决。

关键词：多目标优化，进化算法，适应度计算，精英保留，局部搜索

ABSTRACT

In the past two decades, as a new subject，Multi-Objective Evolutionary Algorithm (MOEA) has attracted much attention, the numerous algorithms have been proposed and MOEA has become the important approach to deal with multi-objective optimization problem （MOP）of engineering design and science research. Many aspects of MOEA have been extensively investigated, however, some problems are still not considered very well. For example,under the condition that many algorithms are brought up, the methods that compare the performance between the algorithms have become very prominent. The main principles of two popular algorithms were analyzed in this paper.

The main work of this paper can be sumrised as the following:

1.A brief review of the history and current studies of MOEA was brought out.All common algorithms have been distributed into several sorts.

2 MOP and the relational technique of MOEA was introduced concisely.Then NSGA-II and MOGLS were expounded in detail.

3 NSGA-II and MOGLS were used for solving the same Multi-Objective scheduling problem separately and their sesults was evaluated by C norm, through this ,the advantage and defect of these two algorithms have been emerged.

MOOP still poses the challenges for algorithm design, visualization and implementation. The dynamic MOP is seldom considered for its time-varying nature. The effective pMOEA is very sparse and the MOEA combining quantum computing and differential evolution is still in the infancy period. The applications of MOEA should be extended continuously in the near future. The theory analysis of MOEA is more complicated than MOEA itself and should be considered through the hard works of researchers majoring in computers and mathematics et al.

KEY WORDS: multi-objective optimizatio n,evo lutio nary algorithm,fitness calculating,elitism duplication,local search