Ant colony optimization (ACO) is a meta-heuristic that is inspired by real ants that are
capable of exploring shortest paths, which inspires researchers to apply it for solving …

Ant colony optimization (ACO) is a metaheuristic algorithm that handles complex
optimization problems. ACO simulates the behavior of ants foraging in the real world to find …

Over the past decades, there is an explosion of data composed by huge information,
because of rapid growing up of computer and database technologies. Ordinarily, this …

Abstract Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic
algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired …

Successful applications coming from biologically inspired algorithm like Ant Colony
Optimization (ACO) based on artificial swarm intelligence which is inspired by the collective …

This paper presents a new variant of ant colony optimization (ACO), called enRiched Ant
Colony Optimization (RACO). This variation tries to consider the previously traversed edges …

This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant
colony and support vector machine. A new direction for ant colony optimisation is to optimise …

The ant colony optimization algorithm (ACO) was initially developed to be a metaheuristic for
combinatorial optimization problem. In scores of experiments, it is confirmed that the …

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 …

Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the
foraging behaviour of real ant colonies. The ants deposit pheromone on the ground in order …