In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

This paper aims at integrating machine learning techniques into meta-heuristics for solving
combinatorial optimization problems. Specifically, our study develops a novel efficient …

This review discusses and analyses three main contributions championed by Professor
Mladenović. These include variable neighborhood search (VNS), variable formulation space …

Finding the best sequence of local search operators that yields the optimal performance of
Variable Neighborhood Search (VNS) is an important open research question in the field of …

Additive manufacturing–also known as 3D printing–is a manufacturing process that is
attracting more and more interest due to high production rates and reduced costs. This …

This article presents a multi-agent framework for optimization using metaheuristics, called
AMAM. In this proposal, each agent acts independently in the search space of a …

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 …

Crowd-ship** is an innovative delivery model, based on the sharing economy concept. In
this framework, delivery operations are carried out by using existing underused resources …

Many businesses offer multiple products or services that are interdependent, in which the
demand for one is often affected by the prices of others. This article considers a revenue …