Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

This paper proposes an adaptive two-stage large-scale multi-objective evolutionary
algorithm, in which a neural network-based accelerating optimizer is designed in the first …

With satellite quantity and quality development in recent years, remote sensing products in
vast areas are becoming widely used in more and more fields. The acquisition of large …

Optimization problem of space scale expansion widely exists in practical applications, such
as transportation, logistics, scheduling, social networks, etc. According to different expansion …

Due to the curse of dimensionality, the search efficiency of existing operators in large-scale
decision space deteriorates dramatically. The competitive swarm optimizer-based …

In large-scale evolutionary multiobjective optimization, the size of the search space expands
exponentially as the number of decision variables increases, which makes it more difficult to …

When tackling large-scale multi-objective problems (LSMOPs), the computational budget
could be wasted by traditional offspring generators that explore the search space in a nearly …

For large-scale multi-objective optimization problems, the trade-off between convergence
and diversity brings significant challenges for researchers. Most of the reproduction …

Evolutionary algorithms excel at versatile optimization for complex (eg, multiobjective)
problems but can be computationally expensive, especially in high-dimensional scenarios …