文献类型：期刊文献

中文题名：基于信息共享的改进双归档高维多目标进化算法

英文题名：Improved Two-archive High-dimensional Multi-objective Evolutionary Algorithm Based on Information Sharing

作者：丁炜超[1];李佳宁[1];顾春华[1];刘佳豪[1];董文波[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2026

卷号：37

期号：3

起止页码：1143

中文期刊名：软件学报

外文期刊名：Journal of Software

收录：;EI(收录号：20262721035703);北大核心:【北大核心2023】；

基金：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(24ZR1415200,23ZR1414900,22ZR1416500)。

语种：中文

中文关键词：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

外文关键词：high-dimensional multi-objective optimization problem(MaOP);two-archive evolutionary algorithm;archive truncation strategy;population reflection update;information sharing mechanism

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.
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 density estimation is adopted to eliminate redundant solutions from the archive,thus maintaining selection pressure during the evolutionary process.Finally,a boundary-solution-driven information compensation mechanism is introduced to facilitate information exchange between the convergence and diversity archives,enabling effective complementarity among individuals in the population.In this study,Two-Arch/IS is benchmarked against several representative algorithms on 69 widely used test instances and real-world problems with 2 to 20 objectives.Experimental results demonstrate that the proposed algorithm effectively addresses the conflict between convergence and diversity in highdimensional many-objective optimization and exhibits superior performance across multiple evaluation metrics.