In surrogate-assisted multi-/many-objective evolutionary optimization, each solution normally has an approximated value on each objective, resulting in increased difficulties in selecting solutions…
The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading ability, the influence maximizatio…
Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision vari…
Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis under…
Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious s…
Sparse multiobjective optimization problems (MOPs) have become increasingly important in many applications in recent years, e.g., the search for lightweight deep neural networks and high-dimensiona…
In preference-based optimization, knee points are considered the naturally preferred tradeoff solutions, especially when the decision maker has little a priori knowledge about the problem to be sol…
Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demo…
Minimax optimization is a widely-used formulation for robust design in multiple operating or environmental scenarios, where the worst-case performance among multiple scenarios is the optimization o…
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning. One is that federated lea…