As renewable power rapidly reshapes global electricity systems, engineers face a growing challenge: how to operate increasingly complex grids with ...

Abstract: Inaccurate heuristic components or highly complex obstacle scenarios are challenges for traditional heuristic pathfinding algorithms since the heuristic function is simple, e.g., based on ...

Identified and explained in detail the gaps and possible future works for improvement in two popular research papers that used heuristic and meta-heuristic algorithms to solve multi-objective vehicle ...

Active learning enables prediction models to achieve better performance faster by adaptively querying an oracle for the labels of data points. Sometimes the oracle is a human, for example when a ...

ABSTRACT: This study presents a two-echelon inventory routing problem (2E-IRP) with an end-of-tour replenishment (ETR) policy whose distribution network consists of a supplier, several distribution ...

A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle ...

This project encompasses a comprehensive suite of algorithms designed to tackle the classic TSP, providing solutions via various heuristics, matheuristics, and exact optimization models. Leveraging ...

In constructing the Support Vector Regression (SVR) model, the Radial Basis Function (RBF) kernel was selected. Due to the significant influence of the penalty factor C and the RBF kernel function ...

Abstract: Path planning in three-dimensional space is an important field of machine learning algorithm research. At present, there are many path planning algorithms, such as heuristic search algorithm ...