Page 180 - 《软件学报》2026年第3期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
2026,37(3):1143−1169 [doi: 10.13328/j.cnki.jos.007496] [CSTR: 32375.14.jos.007496] http://www.jos.org.cn
©中国科学院软件研究所版权所有. Tel: +86-10-62562563

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基于信息共享的改进双归档高维多目标进化算法

丁炜超, 李佳宁, 顾春华, 刘佳豪, 董文波

(华东理工大学 信息科学与工程学院, 上海 200237)
通信作者: 董文波, wbdong@ecust.edu.cn

摘 要: 高维多目标优化问题 (many-objective optimization problem, MaOP) 广泛存在于科学研究和工程应用领域.
受高维目标冲突引起的非支配解集数量呈指数增加影响, 传统的多目标进化算法在求解 MaOP 时面临计算复杂度
增加、解质量降低等困难. 为此, 提出一种基于信息共享的改进双归档高维多目标进化算法 (improved two-archive
high-dimensional multi-objective evolutionary algorithm based on information sharing, Two-Arch/IS), 旨在利用双归档
算法计算复杂度低、收敛及多样性独立优化等优势特性, 实现高维多目标优化问题的高效求解. 相较于传统的算

法, 首先, Two-Arch/IS 基于空间划分的子种群互映更新策略实现档案库的维护, 进一步增强种群的多样性表现; 其
次, 利用基于角度选择与转移密度估计的存档截断策略移除档案库中冗余解, 在进化过程中保持算法的选择压力;
最后, 在种群进化过程中引入边界解驱动的信息补偿机制, 增强收敛性存档和多样性存档间的信息交流, 实现种群
个体间的优势互补. 将 Two-Arch/IS 与其他代表性的算法一同在 69 个具有 2–20 个目标的基准测试与真实世界问
题上进行性能对比实验. 实验结果表明, Two-Arch/IS 算法在高维多目标优化问题上能够有效克服种群收敛性与多
样性的冲突, 并在不同性能评价指标上均表现出明显优势.
关键词: 高维多目标优化问题; 双归档进化算法; 存档截断策略; 种群互映更新; 信息共享机制
中图法分类号: TP301

中文引用格式: 丁炜超, 李佳宁, 顾春华, 刘佳豪, 董文波. 基于信息共享的改进双归档高维多目标进化算法. 软件学报, 2026,
37(3): 1143–1169. http://www.jos.org.cn/1000-9825/7496.htm
英文引用格式: Ding WC, Li JN, Gu CH, Liu JH, Dong WB. Improved Two-archive High-dimensional Multi-objective Evolutionary
Algorithm Based on Information Sharing. Ruan Jian Xue Bao/Journal of Software, 2026, 37(3): 1143–1169 (in Chinese). http://www.jos.
org.cn/1000-9825/7496.htm

Improved Two-archive High-dimensional Multi-objective Evolutionary Algorithm Based on
Information Sharing
DING Wei-Chao, LI Jia-Ning, GU Chun-Hua, LIU Jia-Hao, DONG Wen-Bo
(School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China)
Abstract: Multi-objective optimization problem (MaOP) is widely encountered in scientific research and engineering applications. Due to
the exponential increase in the number of non-dominated solutions caused by objective conflicts, traditional multi-objective evolutionary
algorithms face challenges such as increased computational complexity and degraded solution quality when solving MaOPs. To address
these issues, this study proposes an improved two-archive high-dimensional multi-objective evolutionary algorithm based on information
sharing, Two-Arch/IS, for the efficient solution of MaOP. The proposed algorithm leverages the inherent advantages of the two-archive
framework, including low computational complexity and independent optimization of convergence and diversity. Distinct from traditional
algorithms, archive maintenance in Two-Arch/IS is achieved through a subpopulation reflection and update strategy based on space
partitioning, which enhances population diversity. Furthermore, an archive truncation strategy based on angle selection and shift-based

* 基金项目: 国家自然科学基金 (62403201); 上海市基础研究特区计划 (22TQ1400100-16); 上海市自然科学基金 (24ZR1415200, 23ZR1414
900, 22ZR1416500)
收稿时间: 2024-05-07; 修改时间: 2024-10-14, 2025-05-25; 采用时间: 2025-06-20; jos 在线出版时间: 2025-12-10
CNKI 网络首发时间: 2025-12-11