Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …

Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …

Reliable data measurement is considered to be one of the critical ingredients for variant
Internet of Things (IoT) applications. Gaining full knowledge of measurement data is …

Existing methods for tensor completion (TC) have limited ability for characterizing low-rank
(LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes …

Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …

Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive
optimization, which is useful and efficient when the objective function of the optimization …

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from
the laborious model selection problem due to their high model sensitivity. In particular, for …

There are many important fields involving the multilinear system identification. A great
number of parameters to be identified is an important challenge, leading to the need for …