| 解决多目标优化问题的差分进化算法研究进展 |
| |
| 引用本文： | 叶洪涛,罗飞,许玉格.解决多目标优化问题的差分进化算法研究进展[J].控制理论与应用,2013,30(7):922-928. |
| |
| 作者姓名： | 叶洪涛 罗飞 许玉格 |
| |
| 作者单位： | 1. 广西工学院电气与信息工程学院,广西柳州,545006
2. 华南理工大学自动化科学与工程学院,广东广州,510640 |
| |
| 基金项目： | This work was supported by the Key Project of Chinese Ministry of Education (No.212135), Guangxi Natural Science Foundation (Nos.2012GXNSFB A053165), the Project of Education Department of Guangxi Autonomous Region (Nos. 201203YB131, 201202ZD071), Doctoral Initiating Project of Guangxi University of Technology (No. 11Z09), and the Fundamental Research Funds for the Central Universities (No. 20112M0126). |
| |
| 摘 要： | 差分进化(differential evolution,DE)是一种简单但功能强大的进化优化算法.由于其优秀的性能,其诞生之日起就吸引了各国研究人员的关注.作为一种基于群体的全局性启发式搜索算法,差分进化算法在科学和工程中有许多成功的应用.本文对解决多目标优化问题的差分进化算法研究进行了综述,对差分进化的基本概念进行了详细的描述,给出了几种解决多目标优化问题的差分进化算法变体,并且给出了差分进化算法解决多目标优化问题的理论分析,最后,给出了差分进化算法解决多目标优化问题的工程应用,并指出了未来具有挑战性的研究领域.
|
| 关 键 词： | 多目标优化 差分进化 进化算法 启发式 Pareto优化 |
| 收稿时间： | 2/3/2012 12:00:00 AM |
| 修稿时间： | 2013/3/31 0:00:00 |
Differential evolution for solving multi-objective optimization problems: a survey of the state-of-the-art |
| |
| YE Hong-tao,LUO Fei and XU Yu-ge.Differential evolution for solving multi-objective optimization problems: a survey of the state-of-the-art[J].Control Theory & Applications,2013,30(7):922-928. |
| |
| Authors: | YE Hong-tao LUO Fei and XU Yu-ge |
| |
| Affiliation: | School of Electrical and Information Engineering, Guangxi University of Technology,College of Automation Science and Technology, South China University of Technology,College of Automation Science and Technology, South China University of Technology |
| |
| Abstract: | Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm. It has drawn the attention of researchers all round the globe with its perfect performance since its inception. As a global search of metaheuristics based on population, DE has many successful scientific and engineering applications. A survey of DE for solving multi-objective optimization problems (MOPs) is presented. A detailed review of the basic concepts of DE is provided. Several important variants of DE for solving MOPs are presented. Moreover, the theoretical analyses on DE for solving MOPs are provided. Finally, the engineering applications of DE for solving MOPs and its future challenging field are also pointed out in the remainder of this paper. |
| |
| Keywords: | multiobjective optimization differential evolution evolutionary algorithms metaheuristics Pareto optimality |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《控制理论与应用》浏览原始摘要信息 |
|
点击此处可从《控制理论与应用》下载全文 |
|
