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 …

Although ant colony optimization (ACO) has successfully been applied to a wide range of
optimization problems, its high time-and space-complexity prevent it to be applied to the …

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 …

Ant colony optimization is a metaheuristic approach belonging to the class of model-based
search algorithms. In this paper, we propose a new framework for implementing ant colony …

Ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in
the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions …

Abstract Ant Colony Optimisation (ACO) is one of the well-known swarm intelligence
techniques in the literature. This chapter discusses the inspiration and mathematical model …

ANTS–The International Workshop on Ant Colony Optimization and Swarm Intelligence is
now at its? fth edition. The series started in 1998 with the-ganization of ANTS 1998. At that …

Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the
behavior of real ant colonies. In this paper, we review the underlying ideas of this approach …

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have
been applied to the solution of difficult discrete optimization problems. We put these …

Ant Colony Optimization (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based
on behavior of biological ants. Pheromone laying and selection of shortest route with the …