Szczegóły publikacji
Opis bibliograficzny
New variants of SDLS algorithm for LABS problem dedicated to GPGPU architectures / Dominik ŻUREK, Kamil PIĘTAK, Marcin PIETROŃ, Marek KISIEL-DOROHINICKI // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 1 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12742. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77960-3; e-ISBN: 978-3-030-77961-0. — S. 206–212. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134699 |
|---|---|
| Data dodania do BaDAP | 2021-07-28 |
| DOI | 10.1007/978-3-030-77961-0_18 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2021 |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
Low autocorrelation binary sequence (LABS) remains an open hard optimisation problem that has many applications. One of the promising directions for solving the problem is designing advanced solvers based on local search heuristics. The paper proposes two new heuristics developed from the steepest-descent local search algorithm (SDLS), implemented on the GPGPU architectures. The introduced algorithms utilise the parallel nature of the GPU and provide an effective method of solving the LABS problem. As a means for comparison, the efficiency between SDSL and the new algorithms is presented, showing that exploring the wider neighbourhood improves the results.
