Problem
Research questions and friction points this paper is trying to address.
Dynamic multi-objective optimization
Objective-dimensional variations
Pareto front
Algorithm adaptability
An Unbounded Archive-based Transfer Strategy for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
🤖 AI Summary
本文针对动态多目标优化中目标数量变化导致适应性下降的问题，提出了一种无界存档转移策略UATS，并通过实验验证了其有效提升算法对环境变化的恢复能力和适应性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
unbounded archive-based transfer strategy
dynamic multi-objective optimization
variable number of objectives
historical evolutionary information
Pareto front
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