[1]孙越泓,王 丹.基于新约束集成的差分进化算法[J].南京师范大学学报(自然科学版),2019,42(04):1-11.[doi:10.3969/j.issn.1001-4616.2019.04.001]
 Sun Yuehong,Wang Dan.Differential Evolutionary Algorithm Based on New Ensemble ofConstraint Handing Techniques[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(04):1-11.[doi:10.3969/j.issn.1001-4616.2019.04.001]
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基于新约束集成的差分进化算法()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年04期
页码:
1-11
栏目:
·数学与计算机科学·
出版日期:
2019-12-30

文章信息/Info

Title:
Differential Evolutionary Algorithm Based on New Ensemble ofConstraint Handing Techniques
文章编号:
1001-4616(2019)04-0001-11
作者:
孙越泓12王 丹1
(1.南京师范大学数学科学学院,江苏 南京 210023)(2.江苏省大规模复杂系统数值模拟重点实验室,江苏 南京 210023)
Author(s):
Sun Yuehong12Wang Dan1
(1. School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems,Nanjing 210023,China)
关键词:
约束优化差分进化算法约束处理技术集成
Keywords:
constrained optimizationdifferential evolutionary algorithmensemble of constraint handling techniques
分类号:
TP391; TN911.7
DOI:
10.3969/j.issn.1001-4616.2019.04.001
文献标志码:
A
摘要:
提出基于新约束集成的差分进化算法用于求解带约束的优化问题. 在产生新个体的阶段,算法采用 3种不同的突变策略. 利用不同的约束处理技术对新个体进行选择,并通过引入局部搜索,增强算法局部寻优能力,避免算法陷入局部最优. 该算法在CEC 2017的28个基准函数上进行数值实验,并且与其他较为先进的算法进行比较,实验结果显示,新算法在求解精度上表现较好.
Abstract:
In this paper,a differential evolutionary algorithm based on new ensemble of constraint handing techniques is proposed to solve optimization problems with constraints. At the stage of generating new individuals,the algorithm adopts three different mutation strategies. Different constraint handing techniques are used to select new individuals,and local search is introduced to enhance the local optimization ability and avoid the algorithm falling into local optimum. Numerical experiments are carried out on 28 benchmark functions from CEC 2017 and compared with other advanced algorithms. The results show that the new algorithm performs better in solution accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-05-12.
基金项目:国家自然科学基金(11871279、61971234)、教育部人文社会科学青年基金(12YJCZH179)、江苏省教育厅高校自然科学研究重大项目(16KJA110001).
通讯联系人:孙越泓,博士,副教授,研究方向:智能优化研究. E-mail:05234@njnu.edu.cn
更新日期/Last Update: 2019-12-31