发表机构
School of Artificial Intelligence, Shenzhen University; Shenzhen ZTE Software Co., Ltd.（深圳大学人工智能学院; 深圳市中兴软件有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对目标数量变化的动态多目标优化，提出无界存档迁移策略UATS，通过重用历史非支配解提升环境变化后的恢复与适应性。
AI 中文摘要
目标数量变化的动态多目标优化是困难的，因为目标维度的变化可能显著改变帕累托前沿并降低算法适应性。本文提出了一种无界存档迁移策略（UATS），该策略在每个环境阶段内维护一个无界后代解存档，并在目标变化发生时提取可行的非支配解作为可迁移精英。UATS被嵌入SPEA2SDE以构建UATS-SPEA2SDE，使算法能够重用历史进化信息，同时保留基于位移密度估计的收敛性和多样性优势。在三种目标变化设置下的四个基准问题上进行了实验，将UATS-SPEA2SDE与基于重启的SPEA2SDE基线及四种代表性动态多目标优化算法进行了比较。结果表明，存档引导的迁移改善了环境变化后的恢复能力，并增强了对目标数量变化的适应性。
英文摘要
Dynamic multi-objective optimization with a variable number of objectives is difficult because objective-dimensional variations may significantly change the Pareto front and degrade algorithm adaptability. This paper proposes an unbounded archive-based transfer strategy (UATS), which maintains an unbounded archive of offspring solutions within each environment stage and extracts feasible nondominated solutions as transferable elites when objective changes occur. UATS is embedded into SPEA2SDE to construct UATS-SPEA2SDE, enabling the algorithm to reuse historical evolutionary information while retaining the convergence and diversity advantages of shift-based density estimation. Experiments are conducted on four benchmark problems under three objective-changing settings, where UATS-SPEA2SDE is compared with a restart-based SPEA2SDE baseline and four representative dynamic multi-objective optimization algorithms. The results indicate that the archive-guided transfer improves recovery after environmental changes and enhances adaptability to objective-number variations.
