Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the
social behavior of ant colonies for solving optimization problems. When facing large and …

The purpose of this paper is to propose effective parallelization strategies for the Ant Colony
Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max–Min Ant …

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 …

Combinatorial optimization problems arise in many disciplines, both in the basic sciences
and in applied fields such as engineering and economics. One of the most popular …

Acquiring a functional comprehension of the deregulation of cell signaling networks in
disease allows progress in the development of new therapies and drugs. Computational …

This article presents an approach for the efficient and transparent parallelization of a large
class of swarm algorithms, specifically those where the multiagent paradigm is used to …

Ant Colony Optimization (ACO) is a kind of meta-heuristics algorithm, which simulates the
social behavior of ants and could be a good alternative to existing algorithms for NP hard …

Ant Colony Optimization algorithm is a magnificent heuristics technique based on the
behavior of ants. Parallel computing is a means to achieve the desired results in …

The purpose of this paper is to propose an effective implementation of the Ant Colony
Optimization metaheuristic on actual shared-memory parallel computers. We deal with the …

Ant colony optimization is a population-based search technique that makes use of meta-
heuristics. ACO algorithms are very useful for optimization problems, as they examine …