Szczegóły publikacji
Opis bibliograficzny
Distributed ant colony optimization based on actor model / Mateusz STARZEC, Grażyna STARZEC, Aleksander BYRSKI, Wojciech TUREK // Parallel Computing ; ISSN 0167-8191. — 2019 — vol. 90 art. no. 102573, s. 1–9. — Bibliogr. s. 9, Abstr. — Publikacja dostępna online od: 2019-10-18
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 125534 |
|---|---|
| Data dodania do BaDAP | 2019-11-09 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.parco.2019.102573 |
| Rok publikacji | 2019 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Parallel Computing |
Abstract
The parallelization of metaheuristics and care for the efficient use of the available infrastructure is very popular in the case of population-based algorithms (e.g., evolutionary ones), as many of them have structures intrinsically easy for parallelization. However, swarm computing algorithms (ACO in particular) must use certain global knowledge in order to be properly implemented (e.g., a pheromone matrix in the case of ACO algorithms). Thus, the parallelization of ACO is known to be difficult to realize. In this paper, we propose an actor-based approach for constructing an efficient and robust ACO implementation that leverages the HPC infrastructure. The presented results show the ability to be scaled for up to 30 nodes, and the relevant results support the claim that the implemented algorithm is equal to the original Ant System algorithm. Improving it further and increasing its scalability with the planned asynchrony in the pheromone matrix updates is envisioned as a direct future work.
