Nonsmooth gradient ascent on layered hypervolume and magnitude indicators moves sets to the Pareto front.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
citing papers explorer
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Nonsmooth Set-Gradient Ascent to the Pareto Front via Layered Hypervolume and Magnitude Indicators
Nonsmooth gradient ascent on layered hypervolume and magnitude indicators moves sets to the Pareto front.
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Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.