[1]梁静,刘睿,瞿博阳,等.进化算法在大规模优化问题中的应用综述[J].郑州大学学报(工学版),2018,39(03):15-21.[doi:10.13705/j.issn.1671-6833.2017.06.016]
 Liang Jing,Liu Rui,Qu Boyang,et al.A Survey of Evolutionary Algorithms for  Large Scale Optimization Problem[J].Journal of Zhengzhou University (Engineering Science),2018,39(03):15-21.[doi:10.13705/j.issn.1671-6833.2017.06.016]
点击复制

进化算法在大规模优化问题中的应用综述()
分享到:

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年03期
页码:
15-21
栏目:
出版日期:
2018-05-10

文章信息/Info

Title:
A Survey of Evolutionary Algorithms for  Large Scale Optimization Problem
作者:
梁静刘睿瞿博阳岳彩通
1.郑州大学电气工程学院,河南郑州,450001;2.中原工学院电子信息学院,河南郑州,450007
Author(s):
Liang Jing1Liu Rui1Qu Boyang2Yue Caitong1
1. School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, 450001; 2. School of Electronic Information, Zhongyuan Institute of Technology, Zhengzhou, Henan, 450007
关键词:
大规模优化问题进化算法协同进化种群初始化基准测试函数
Keywords:
large scale optimization problemevolutionary algorithmcooperative coevolutionpopulation initializationbenchmark function
DOI:
10.13705/j.issn.1671-6833.2017.06.016
文献标志码:
A
摘要:
针对大规模问题的特点,对已有的大规模优化算法进行了简单的分析.主要介绍算法的初始化方法、不分组策略、静态分组策略、动态分组策略、自适应分组策略、大规模优化算法测试函数集以及算法结果的对比等方面;侧重描述优化算法的搜索策略、更新策略、突变策略和协同进化策略,并列出大规模优化算法测试函数集的特点及优化算法的评价方法;最后,给出了目前大规模优化问题的几个研究难点。
Abstract:
Based on the characterisities of large-scale problems, lager-scale optimization were grossly analyzed. This paper  introduced some methods for lager-scale problems.The methods included the initialization method, decomposition strategy, updating strategy and so on. This paper mainly focued on the search strategy, update strategy, mutation strategy and cooperative coevolution. Meanwhile, the characteristics of lager-scale optimization algorithm testing function set and evaluation method were listed. Finally, the future research directions were given.

相似文献/References:

[1]李章晓,宋微,田野.基于深度学习和进化计算的外汇预测与投资组合优化[J].郑州大学学报(工学版),2019,40(01):92.[doi:10.13705/j.issn.1671-6833.2019.01.014]
 Li Zhangxiao,Song Wei,Tian Ye.Exchange Rate Forecasting and Portfolio Optimization Based on Deep Learning and Evolutionary Computation[J].Journal of Zhengzhou University (Engineering Science),2019,40(03):92.[doi:10.13705/j.issn.1671-6833.2019.01.014]

更新日期/Last Update: 2018-05-03