[1]汤可宗,李佐勇,詹棠森,等.一种基于Pareto关联度支配的多目标粒子群优化算法[J].南京理工大学学报(自然科学版),2019,43(04):439-446.[doi:10.14177/j.cnki.32-1397n.2019.43.04.009]
 Tang Kezong,Li Zuoyong,Zhan Tangsen,et al.A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination[J].Journal of Nanjing University of Science and Technology,2019,43(04):439-446.[doi:10.14177/j.cnki.32-1397n.2019.43.04.009]
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一种基于Pareto关联度支配的多目标粒子群优化算法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年04期
页码:
439-446
栏目:
出版日期:
2019-08-24

文章信息/Info

Title:
A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination
文章编号:
1005-9830(2019)04-0439-08
作者:
汤可宗1李佐勇2詹棠森1李 芳1姜云昊1
1.景德镇陶瓷大学 信息工程学院,江西 景德镇 333403; 2.工业机器人应用福建省高校工程研究中心 闽江学院,福建 福州 350108
Author(s):
Tang Kezong1Li Zuoyong2Zhan Tangsen1Li Fang1Jiang Yunhao1
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 2.Industrial Robot Application of Fujian University Engineering Research Center,Minjiang University,Fuzhou 350108,China
关键词:
多目标优化 粒子群优化 Pareto支配 关联度 多样性
Keywords:
multi-objective optimization particle swarm optimization Pareto domination correlation degree diversity
分类号:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2019.43.04.009
摘要:
为提高多目标优化算法的收敛性和多样性,提出一种基于Pareto关联度支配的多目标粒子群优化算法(MOPSO-PCD)。该算法在严格遵守传统Pareto支配规则基础上,将灰色关联分析方法融入非劣支配解的进化过程,设计了一种新颖的Pareto关联度支配规则。该支配规则作用于全局最优粒子的选择过程,具有关联度最大的全局最优粒子将引领粒子群体向着真实Pareto前沿不断逼近。同时,将该支配规则应用于外部档案中非劣支配解的维护过程,可减少或避免最终解集多样性的损失,从而维护好外部档案中非劣解的分布过程。仿真实验表明,与被比较算法在ZDT和DTLZ等系列测试函数相比,MOPSO-PCD能够获得更好的Pareto最优前沿分布特性和较快的收敛效率。
Abstract:
In order to improve the convergence and diversity of multi-objective optimization algorithm,this paper proposes a multi-objective particle swarm optimization algorithm based on Pareto correlation degree domination(MOPSO-PCD). On the basis of strict compliance with the traditional Pareto domination programming,MOPSO-PCD integrates the grey relational analysis method into the evolutionary process of non-dominated solutions,and designs a new Pareto correlation association degree domination programming. In the selection process of the global optimal particle,the optimal particle with the largest correlation degree leads the particle swarm to approach the true Pareto frontier distribution. At the same time,the domination programming can also maintain the diversity of the non -dominated solutions in the external archive,and reduce or avoid the loss of the diversity of the final solution set,thus maintaining the distribution process of the non-dominated solutions of the external archive. Simulation results of ZDT and DTLZ test functions show that MOPSO-PCD has better Pareto optimal frontier distribution and faster convergence efficiency than three comparison algorithms.

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备注/Memo

备注/Memo:
收稿日期:2019-03-25 修回日期:2019-05-12
基金项目:国家自然科学基金(61662037,71763013); 江西省杰出青年人才计划资助(20171bcb23069); 江西省教育厅科技项目(GJJ170764); 江西省青年科学基金项目(2016BAB212042); 工业机器人应用福建省高校工程研究中心开放基金资助(MJUKF-IRA201808)
作者简介:汤可宗(1978-),男,博士,副教授,主要研究方向:智能信息处理,E-mail:tangkezong@126.com。
引文格式:汤可宗,李佐勇,詹棠森,等. 一种基于Pareto关联度支配的多目标粒子群优化算法[J]. 南京理工大学学报,2019,43(4):439-446.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-09-30