High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

The Ant Colony Optimization (ACO) metaheuristic [1] is a constructive population-based approach based on the social behavior of ants. As it is acknowledged as a powerful method to solve academic and industrial combinatorial optimization problems, a considerable amount of research is dedicated to improving its performance. Among the proposed solutions, we find the use of parallel computing to re...

A Parallel Implementation of Ant Colony Optimization

A survey on parallel ant colony optimization

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 complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-s...

Eecient Parallel Programming with Algorithmic Skeletons

Algorithmic skeletons are polymorphic higher-order functions representing common parallelization patterns and implemented in parallel. They can be used as the building blocks of parallel and distributed applications by integrating them into a sequential language. In this paper, we present a new approach to programming with skeletons. We integrate the skeletons into an imperative host language e...

Parallel Implementation of an Ant Colony Optimization Metaheuristic with Openmp

This paper presents a parallel implementation of an ant colony optimization metaheuristic for the solution of an industrial scheduling problem in an aluminum casting center. The usefulness and efficiency of the algorithm, in its sequential form, to solve that particular optimization problem has already been shown in previous work. However, even if this method, as well as metaheuristics in gener...