  In Very Long Baseline Interferometry (VLBI), signals from multiple antennas
combine to create a sparsely sampled virtual aperture, its effective diameter
determined by the largest antenna separation. The inherent sparsity makes VLBI
imaging an ill-posed inverse problem, prompting the use of algorithms like the
Multiobjective Evolutionary Algorithm by Decomposition (MOEA/D), as proposed in
the first paper of this series. This study focuses on extending MOEA/D to
polarimetric and time dynamic reconstructions, particularly relevant for the
VLBI community and the Event Horizon Telescope Collaboration (EHTC). MOEA/D's
success in providing a unique, fast, and largely unsupervised representation of
image structure serves as the basis for exploring these extensions. The
extension involves incorporating penalty terms specific to total intensity
imaging, time-variable, and polarimetric variants within MOEA/D's
multiobjective, evolutionary framework. The Pareto front, representing
non-dominated solutions, is computed, revealing clusters of proximities.
Testing MOEA/D with synthetic datasets representative of EHTC's main targets
demonstrates successful recovery of polarimetric and time-dynamic signatures
despite sparsity and realistic data corruptions. MOEA/D's extension proves
effective in the anticipated EHTC setting, offering an alternative and
independent claim to existing methods. It not only explores the problem
globally but also eliminates the need for parameter surveys, distinguishing it
from Regularized Maximum Likelihood (RML) methods. MOEA/D emerges as a novel
and useful tool for robustly characterizing polarimetric and dynamic signatures
in VLBI datasets with minimal user-based choices. Future work aims to address
the last remaining limitation of MOEA/D, specifically regarding the number of
pixels and numerical performance, to establish it within the VLBI data
reduction pipeline.
