生物地理学优化算法在非线性优化中的应用(任务书,开题报告,论文12000字)
摘要
随着工业和科学理论研究的发展，计算机的普及，各个领域对优化的要求也越来越高，常规的优化算法已远远不能满足实际优化问题。所以研究新型优化算法变得非常具有现实意义。生物地理学优化算法(Biogeography-Based Optimization,BBO)就是一种新型的优化算法，它是由Dan Simon在2008年基于生物地理学理论提出的一种新型智能优化算法，具有良好的收敛性和稳定性。目前，得益于其独特的机制和良好的性能，生物地理学优化算法在智能优化算法领域得到了广泛的关注和重视。作为一个提出才8年的新型优化算法，BBO算法具有非常广阔的应用前景。同时，还有很多的研究工作要做。因此，BBO算法的理论和应用研究在学术和实际工作中具有重要的意义和价值。
本文从生物地理学优化算法的背景出发，介绍了算法的基本理论及算法步骤，并用14个基准函数对该算法进行了性能测试。最后介绍了算法的研究现状和算法未来的研究趋势。
本文第一部分介绍了BBO算法在国内外的研究现状，并提出了未来该算法在不同方向的研究目标。第二部分主要介绍了算法相关背景，包括生物地理学优化算法的提出背景、算法设计原理、算法基于的迁移模型和算法步骤。第三部分介绍了非线性优化的理论知识，并举出了传统优化方法的局限性和BBO算法的优势。第四部分为仿真结果。用14个基准函数对BBO算法的性能进行评估，并与其它7种优化算法进行比较。结果表明BBO算法在处理常规优化问题上的有效性，也表明该算法在处理工程优化问题上是一种十分有效的优化算法。
关键词:生物地理学；进化算法；生物地理学优化算法；非线性优化
Abstract
With the development of industrial and scientific research, the popularity of computers, the requirements of the optimization of the various fields are also getting higher and higher, the conventional optimization algorithm has been far from meeting the actual optimization problems.Therefore, the study of new optimization algorithm is very practical significance.Biogeography-Based Optimization(BBO) is a new intelligent optimization algorithm based on Biogeography theory proposed by Dan Simon in 2008, which has good astringency and stability. At present, due to its unique mechanism and good performance, the optimization algorithm in the field of intelligent optimization algorithm has been widely concerned and valued.As a new parallel search algorithm for 8 years, BBO algorithm has a very broad application prospects. Meanwhile, there are still a lot of research work to be done. Therefore, it has important academic and practical significance of their theoretical and applied research.
Based on the background of BBO, we introduce the basic theory of BBO and algorithm procedure in this paper, and we use 14 benchmark functions to test the performance of the algorithm. At last, this paper introduces the present situation of the algorithm and the future research trend.
The first part of this paper introduces the research status of BBO algorithm at home and abroad, and puts forward the future research objectives of the algorithm in different directions.The second part mainly introduces the background of the algorithm, including the background of the optimization algorithm, the design principle of the algorithm, the migration model based on the algorithm and the steps of the algorithm.The third part introduces the theoretical knowledge of nonlinear optimization, and gives the limitations of the traditional optimization methods and the advantages of BBO algorithm.The fourth part is the simulation results. 14 benchmark functions are used to evaluate the performance of BBO algorithm, and compared with other 7 optimization algorithms.The results show that the BBO algorithm is effective in dealing with the conventional optimization problems. It also shows that the algorithm is an effective optimization algorithm in the process of engineering optimization problems.
Key words:Biogeography;Evolutionaryalgorithms; Biogeography-based optimization; Nonlinear optimization
目录
摘要 I
Abstract II
第1章绪论 1
1.1研究目的及意义 1
1.2 BBO算法的国内外研究现状与应用 2
1.2.1 BBO算法的国内外研究现状 2
1.2.2 BBO算法的应用 3
1.2论文结构安排 4
第2章生物地理学优化算法(BBO) 5
2.1 算法背景 5
2.2 算法设计原理 5
2.3 迁移模型 6
2.4 算法步骤 9
2.4.1 BBO具体算法步骤 9
2.4.2 BBO迁移（Migration）操作 9
2.4.3 BBO突变（Mutation）操作 10
2.5 BBO和其他进化算法的区别 11
第3章非线性优化 12
3.1传统优化方法及比较 12
3.2 BBO算法优势 13
第4章数值仿真及结果分析 15
第5章总结与展望 18
参考文献 19
致谢 21 |
