Evolutionary transfer optimization (ETO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the …
It is foreseeable that the 100 Gb/s/ λ and beyond passive optical network (PON) will be required in future optical access networks to meet the explosive growth of data traffic. The coherent optica…
Decomposition plays a significant role in cooperative coevolution (CC), which shows great potential in large-scale black-box optimization (LSBO). However, current learning-based decomposition algor…
The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detecting occluded and…
By employing the mode decomposition method to study the relative mode evolution characteristics during the stimulated Raman scattering (SRS) process, a numerical model of the core-pumped Raman effe…
A typical coherent front-end may encounter imperfections, such as amplitude imbalance, phase imbalance and skew, between four sampling channels. These receiver (Rx) imperfections, together with tra…
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to…
Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective optimization problem which varies over time. As changes in DMOP may exist some patterns that are predictable, to solve …
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…
The evolutionary algorithm (EA) is a nature-inspired population-based search method that works on Darwinian principles of natural selection. Due to its strong search capability and simplicity of im…