Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA)
that aims to optimize multiple tasks simultaneously within a same problem and output the …

In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary
differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …

Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they
involve the optimization of multiple objectives and the satisfaction of various constraints …

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

Evolutionary multitask optimization is an emerging research topic that aims to solve multiple
tasks simultaneously. A general challenge in solving multitask optimization problems …

Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration
from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a …

Handling conflicting objectives and finding multiple Pareto optimal solutions are two
challenging issues in solving multiobjective optimization problems (MOPs). Inspired by the …