In recent years, the m ulti-task learning for k nowledge graph-based r ecommender system,
termed MKR, has shown its promising performance and has attracted increasing interest …

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

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the
start point to the destination point for each aerial vehicle. With the rapid development of UAV …

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

Most optimization problems of practical significance are typically solved by highly
configurable parameterized algorithms. To achieve the best performance on a problem …

Knowledge transfer (KT) is crucial for optimizing tasks in evolutionary multitask optimization
(EMTO). However, most existing KT methods can only achieve superficial KT but lack the …

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 …

The selection of an algorithm in the real world of the application domain is a challenging
problem as no specific algorithm exists capable of solving all issues to a satisfactory …

This work considers an extended flexible job-shop scheduling problem from a
semiconductor manufacturing environment. To find its high-quality solution in a reasonable …

Binary optimization assumes a pervasive significance in the context of practical applications,
such as knapsack problems, maximum cut problems, and critical node detection problems …