Szczegóły publikacji
Opis bibliograficzny
Exploiting flower constancy in flower pollination algorithm: improved biotic flower pollination algorithm and its experimental evaluation / Paweł KOPCIEWICZ, Szymon ŁUKASIK // Neural Computing & Applications ; ISSN 0941-0643. — 2020 — vol. 32 iss. 16 spec. iss. Real-world optimization problems and meta-heuristics, s. 11999–12010. — Bibliogr. s. 12009-12010, Abstr. — Publikacja dostępna online od: 2019-04-10. — Sz. Łukasik - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 128147 |
|---|---|
| Data dodania do BaDAP | 2020-09-18 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s00521-019-04179-9 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Neural Computing & Applications |
Abstract
Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented. © 2019, The Author(s).
