High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Abstract

Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

Study of Parallel Model of Artificial Bee Colony Algorithm

“…Despite the important drawback, which consists in significant computational costs, namely in a large number of calculations of the fitness function, swarm meta-heuristics can be a suitable choice, if the possibility of parallelization of calculations is available. For example, in [8] parallel ABC algorithm which utilizes MPI programming environment has been proposed and studied; in [9] authors provide brief overview of GPU-based meta-heuristics; in [10] the implementation of high-level parallel AAC is described and explored; in [11] a comprehensive survey on parallel particle swarm optimization (PSO) algorithms is presented along with their parallelization strategies and applications.…”