Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-
heuristics to solve challenging optimization problems. They differ from traditional (meta) …

Carbon peaking and carbon neutrality, which are the significant national strategy for
sustainable development, have attracted considerable attention from production enterprises …

The integration of reinforcement learning technology into meta-heuristic algorithms to
address complex combinatorial optimization problems has attracted much attention in recent …

An urban traffic light scheduling problem (UTLSP) is studied by using problem feature based
meta-heuristics with Q-learning. The goal is to minimize the network-wise total delay time …

This study constructs a reinforcement learning-based hybrid algorithm for Aquila Optimizer
(AO) and improved Arithmetic Optimization Algorithm (IAOA). The point of the hybrid …

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 …

Owning to diverse customer demands and enormous product varieties, mixed shop
production systems are applied in practice to improve responsiveness and productivity …

Data mining and machine learning algorithms' performance is degraded by data of high-
dimensional nature due to an issue called “curse of dimensionality”. Feature selection is a …

This paper introduces a new variant of the metaheuristic algorithm based on the naked mole
rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for …

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported
that hyper heuristics are well applied in combinatorial optimisation problems. As a classic …