Decision Variable Analysis-Guided Differentiated Fuzzy Search for Large-Scale Multi-Objective Optimization
Neural and Evolutionary Computing
Summary
The authors address the challenge of solving large-scale multi-objective optimization problems, which involve many decision variables. They point out that previous fuzzy search methods treated all variables the same, missing that different variables play different roles in the search process. Their new method, DDFS, analyzes these roles and adjusts the search effort accordingly, improving efficiency and results. They also introduced a way to change search behavior over time to balance exploring broadly at first and fine-tuning later. Tests show their approach performs well compared to other methods on problems with up to 1000 variables.
Authors
Boxi Xiao, Hui Bai, Jinhua Zheng, Yu Li, Juan Zou
Abstract
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.