The field of metaheuristics for the application to combinatorial optimization problems is a
rapidly growing field of research. This is due to the importance of combinatorial optimization …

Abstract Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone
trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic …

Research into the applicability of ant-based optimisation techniques for hyper-heuristics is
largely limited. This paper expands upon the existing body of research by presenting a novel …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Swarm intelligence is a relatively new approach to problem solving that takes inspiration
from the social behaviors of insects and of other animals. In particular, ants have inspired a …

Research on a new metaheuristic for optimization is often initially focused on proof-of-
concept applications. It is only after experimental work has shown the practical interest of the …

Ant colony optimization is a technique for optimization that was introduced in the early
1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant …

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs).
Recent works have shown that attention-based RL models outperform recurrent neural …

Optimization techniques inspired by swarm intelligence have become increasingly popular
during the last decade. They are characterized by a decentralized way of working that …

Algorithms for solving hard optimization problems typically have several parameters that
need to be set appropriately such that some aspect of performance is optimized. In this …