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Data for ameta-analysis of theadaptive layer inadaptive largeneighborhood search Renata TurkešKenneth SörensenLars Magnus Hvattum2020 год

Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search
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Аннотация: 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.
Год издания: 2020
Авторы: Renata Turkeš, Kenneth Sörensen, Lars Magnus Hvattum, Eva Barrena, Hayet Chentli, Leandro C. Coelho, Iman Dayarian, Axel Grimault, Anders N. Gullhav, Çağatay Iris, Merve Keskin, Alexander Kiefer, Richard Martin Lusby, Geraldo Regis Mauri, Marcela Monroy‐Licht, Sophie N. Parragh, Juan‐Pablo Riquelme‐Rodríguez, Alberto Santini, Vínicius Gandra Martins Santos, C. B. Thomas
Издательство: Elsevier BV
Источник: Data in Brief
Ключевые слова: Vehicle Routing Optimization Methods, Metaheuristic Optimization Algorithms Research, Transportation Planning and Optimization
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