Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …

Sparse multi-objective optimization problems (SMOPs) frequently exist in a variety of
disciplines such as machine learning, economy, and signal processing. Evolutionary …

The optimization of operating conditions in the polyester polymerization process is crucial for
enhancing the quality of the resulting polyester. A novel multi-objective optimization …

Sparse multi-objective optimization problems (SMOPs) are prevalent in a wide range of
applications, spanning from the fields of science to engineering. Existing sparse …

In large-scale many-objective optimization problems (LMaOPs), the performance of
algorithms faces significant challenges as the number of objective functions and decision …

Sparse large-scale multi-objective optimization problems (LSMOPs), in which most decision
variables of the Pareto-optimal solutions are zero, have become increasingly prevalent in …

Large-scale multiobjective optimization problems have attracted increasing attention in both
engineering applications and scientific research. Academically, large-scale multiobjective …

Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple
conflicting objectives with hundreds or even thousands of decision variables, demand …

Traditional multi-objective evolutionary algorithms (MOEAs) face challenges when
addressing sparse large-scale multi-objective optimization problems (SLSMOPs) with many …

Due to the curse of dimensionality and the unknown sparsity of search spaces, evolutionary
algorithms face immense challenges in approximating optimal solutions for widely studied …