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Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Towards Geometry-Aware Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications.
Pointer Networks Trained Better via Evolutionary Algorithms
With this advantage, this paper is able to at the first time report the results of solving 1000-dimensional TSPs by training a PtrNet on the same dimensionality, which strongly suggests that scaling up the training instances is in need to improve the performance of PtrNet on solving higher-dimensional COPs.
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