This study proposes a robust method to improve the search performance of multi-objective
evolutionary algorithms (MOEAs) using a Pareto-based archiving mechanism and a …

Dividing variables into groups is an intuitive idea for tackling large-scale multi-objective
problems. However, regular grouping methods often suffer from the computationally …

Most existing dominance relations give higher priority to convergence than diversity and
cannot offer reasonable selection pressure according to the evolution status. This easily …

As large-scale multiobjective optimization problems (LSMOPs) contain many decision
variables, most existing large-scale multiobjective optimization algorithms (LSMOEAs) …

Surgical cases assignment problem (SCAP) is among the most investigated interests in
operating room planning. The studies related to the SCAP are mainly focused on the single …

It is well known that many-objective optimization problems (MaOPs) are difficultly to be
balanced diversity and convergence in the search process because diversity and …

As manufacturing shifts towards large-scale production, the size of the workshop increases,
and its search space exponentially expands. It is difficult for existing algorithms to obtain an …

There are huge numbers of real-world optimization problems, which often contain a large
number of decision variables (n> 100) and objective functions (m> 3). Such optimization …

The assessment of cold chain logistics for fresh products can be more precise with high-
dimensional information data, providing valuable insights for the optimization of associated …

Large scale multi-objective optimization problems often involve hundreds or thousands of
decision variables. Regular methods tend to divide decision variables into multiple groups …