决策变量分析引导的差异化模糊搜索用于大规模多目标优化
Decision Variable Analysis-Guided Differentiated Fuzzy Search for Large-Scale Multi-Objective Optimization
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中文总结 AI 辅助
针对大规模多目标优化问题，提出决策变量分析引导的差异化模糊搜索方法DDFS，通过建立变量角色与搜索粒度映射，采用双指标机制动态调整，实验表明该方法能提升高维决策空间优化性能。
中文摘要 AI 辅助
大规模多目标优化问题（LSMOPs）因其高维决策空间而具有挑战性。模糊搜索是提高搜索效率的有效技术，而决策变量分析可揭示变量在促进收敛和保持多样性方面的不同作用。现有模糊搜索方法通常对所有变量采用统一搜索粒度，忽略了变量角色所隐含的异构搜索需求。本文提出了一种决策变量分析引导的差异化模糊搜索方法（DDFS）。该方法在决策变量角色和模糊搜索粒度之间建立了明确映射。利用决策变量分析识别变量角色和搜索敏感性，使不同变量组在生成后代时采用差异化模糊搜索行为。此外，还开发了一种双指标阶段转换机制，在整个进化过程中动态调整模糊更新强度，平衡早期搜索空间压缩和后期收敛细化。在具有多达1000个决策变量的LSMOP和UF基准套件上进行的大量实验表明，DDFS通常能与几种代表性的大规模多目标进化算法取得有竞争力的性能。结果表明，将决策变量角色明确纳入模糊搜索有助于提高高维决策空间中的优化性能。
英文摘要
Large-scale multi-objective optimization problems (LSMOPs) are challenging due to their high-dimensional decision spaces. Fuzzy search is an effective technique for improving search efficiency, while decision variable analysis can reveal the distinct roles of variables in promoting convergence and maintaining diversity. However, existing fuzzy search methods generally employ a uniform search granularity for all variables, overlooking the heterogeneous search requirements implied by variable roles. To address this limitation, this paper proposes a Decision variable analysis-guided Differentiated Fuzzy Search method, termed DDFS. The proposed method establishes an explicit mapping between decision-variable roles and fuzzy search granularities. Decision variable analysis is employed to identify variable roles and search sensitivities, enabling different variable groups to adopt differentiated fuzzy search behaviors during offspring generation. Furthermore, a Dual-Indicator Stage Transition Mechanism is developed to dynamically adjust fuzzy-updating intensity throughout the evolutionary process, balancing early-stage search-space compression and late-stage convergence refinement. Extensive experiments on the LSMOP and UF benchmark suites with up to 1000 decision variables show that DDFS generally achieves competitive performance against several representative large-scale multi-objective evolutionary algorithms. The results suggest that explicitly incorporating decision-variable roles into fuzzy search can help improve optimization performance in high-dimensional decision spaces.
