[1]汪慎文,王佳莹,张佳星,等.SI7:应用精英档案和反向学习的多目标差分进化算法[J].郑州大学学报(工学版),2020,41(06):40-45.[doi:10.13705/j.issn.1671-6833.2020.06.011]
　WANG Shenwen,WANG Jiaying,ZHANG Jiaxing,et al.A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):40-45.[doi:10.13705/j.issn.1671-6833.2020.06.011]
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SI7:应用精英档案和反向学习的多目标差分进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年06期
页码:
40-45
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning
作者:
汪慎文; 王佳莹; 张佳星; 王峰; 王晖
河北地质大学信息工程学院,河北石家庄050031, 河北地质大学人工智能与机器学习研究室,河北石家庄050031, 武汉大学计算机学院,湖北武汉430072, 南昌工程学院信息工程学院,江西南昌330099

Author(s):
WANG Shenwen1; 2;  WANG Jiaying1; 2;  ZHANG Jiaxing1; 2;  WANG Feng3;  WANG Hui4
1.School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China; 2.Laboratory of Artificial Intelligence and Machine Learning, Hebei GEO University, Shijiazhuang 050031, China; 3.School of Computer Science, Wuhan University, Wuhan 430072, China; 4.School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099,China
关键词:
Keywords:
multi-objective optimization;  elite-archive;  opposition-based learning;  differential evolution algorithm;  constrained decomposition with grids
DOI:
10.13705/j.issn.1671-6833.2020.06.011
文献标志码:
A
摘要:
针对多目标优化问题日渐复杂，受集成算法思想的启发，提出一种应用精英档案和反向学习的多目标差分进化算法。该算法通过建立一个外部档案，来保存种群进化过程中的非支配解，采用精英保留策略维持档案规模，提高算法收敛速度。在进化过程中根据反向学习代跳跃概率，使用一般反向学习策略生成反向解，扩大搜索范围，提高种群多样性。利用网格系统确定解的坐标，并根据一定的约束生成交叉池，在交叉池中选择父代个体，利用差分进化算法产生新个体，通过网格约束分解排序算法选择下一代种群。将此算法与其他几个代表性算法在UF测试函数上进行实验，结果表明，所提出的算法多样性及收敛性表现更优。
Abstract:
The multi-objective optimization problem became more and more complex. Inspired by the ensemble algorithm, a multi-objective differential evolution algorithm with elite-archive and opposition-based learning is proposed in this paper. In this algorithm, an external archive was created to save the nondominated solutions in the evolutionary process of the population. It used the preset opposition-based generation jumping andopposition-based learning to generate the different solutions of the individual and stored in the elite archive, to expand the search scope and improve population diversity. The grid was used to determines the coordinates of the solutions, and the restricted mating pool was generated according to certain constraints. The parent solutions were selected in the restricted mating pool to produce the new individual by using differential evolution algorithm, then generated the next iteration population by constrained decomposition with grids sorting. The experimental results showed that the proposed algorithm had strong robustness with the shapes of PFs in solving unconstrained multi-objective optimization problems was superior to some state-of-the-art multi-objective algorithms in diversity and convergence on UF test problems.

参考文献/References:

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更新日期/Last Update: 2021-02-10