GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
MOEA/D: A multiobjective evolutionary algorithm based on de- composition.IEEE Transactions on Evolutionary Computation, 11(6):712–731
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
background 1representative citing papers
A minimal greedy regional zoom method outperforms Pareto and global Bayesian optimization in budget-constrained SBSE, winning or tying in 84-89% of cases at equal budget and even at one-fifth budget, because optimal solutions cluster in a compact decision-space island.
citing papers explorer
-
GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization
GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
-
Zoom, Don't Wander: Why Regional Search Outperforms Pareto Reasoning and Global Optimization in Budget-Constrained SBSE
A minimal greedy regional zoom method outperforms Pareto and global Bayesian optimization in budget-constrained SBSE, winning or tying in 84-89% of cases at equal budget and even at one-fifth budget, because optimal solutions cluster in a compact decision-space island.