Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search
Autor: | Anders Nordby Gullhav, Richard Martin Lusby, Kenneth Sörensen, Marcela Monroy-Licht, Alexander Kiefer, Axel Grimault, Eva Barrena, Iman Dayarian, Merve Keskin, Alberto Santini, Cagatay Iris, Lars Magnus Hvattum, Hayet Chentli, Sophie N. Parragh, Renata Turkeš, Juan-Pablo Riquelme-Rodríguez, Geraldo Regis Mauri, Vinicius Gandra Martins Santos, Charles Thomas, Leandro C. Coelho |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Computer science
Metaheuristics lcsh:Computer applications to medicine. Medical informatics computer.software_genre Domain (software engineering) Set (abstract data type) 03 medical and health sciences 0302 clinical medicine lcsh:Science (General) Implementation Metaheuristic Research question Data Article 030304 developmental biology 0303 health sciences Multidisciplinary Adaptive large neighborhood search Documentation and information Replicate Random effects model Range (mathematics) Meta-analysis lcsh:R858-859.7 Data mining computer Engineering sciences. Technology 030217 neurology & neurosurgery lcsh:Q1-390 |
Zdroj: | Data in Brief Data in brief Turkeš, R, Sörensen, K, Hvattum, L M, Barrena, E, Chentli, H, Coelho, L C, Dayarian, I, Grimault, A, Gullhav, A N, Iris, Ç, Keskin, M, Kiefer, A, Lusby, R M, Mauri, G R, Monroy-Licht, M, Parragh, S N, Riquelme-Rodríguez, J-P, Santini, A, Santos, V G M & Thomas, C 2020, ' Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search ', Data in Brief, vol. 33, 106568 . https://doi.org/10.1016/j.dib.2020.106568 December Data in Brief, Vol 33, Iss, Pp 106568-(2020) |
ISSN: | 2352-3409 |
DOI: | 10.1016/j.dib.2020.106568 |
Popis: | Meta-analysis, a systematic statistical examination that combines the results of several independent studies, has the potential of obtaining problem- and implementation-independent knowledge and understanding of metaheuristic algorithms, but has not yet been applied in the domain of operations research. To illustrate the procedure, we carried out a meta-analysis of the adaptive layer in adaptive large neighborhood search (ALNS). Although ALNS has been widely used to solve a broad range of problems, it has not yet been established whether or not adaptiveness actually contributes to the performance of an ALNS algorithm. A total of 134 studies were identified through Google Scholar or personal e-mail correspondence with researchers in the domain, 63 of which fit a set of predefined eligibility criteria. The results for 25 different implementations of ALNS solving a variety of problems were collected and analyzed using a random effects model. This dataset contains a detailed comparison of ALNS with the non-adaptive variant per study and per instance, together with the meta-analysis summary results. The data enable to replicate the analysis, to evaluate the algorithms using other metrics, to revisit the importance of ALNS adaptive layer if results from more studies become available, or to simply consult the ready-to-use formulas in the summary file to carry out a meta-analysis of any research question. The individual studies, the meta-analysis and its results are described and interpreted in detail in Renata Turkeš, Kenneth Sörensen, Lars Magnus Hvattum, Meta-analysis of Metaheuristics: Quantifying the Effect of Adaptiveness in Adaptive Large Neighborhood Search, in the European Journal of Operational Research. Keywords: meta-analysis, metaheuristics, adaptive large neighborhood search |
Databáze: | OpenAIRE |
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