Abstract
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.
Highlights
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Meta-analysis, a systematic statistical examination that combines the results of several independent studies, has the potential of obtaining problem- and implementationindependent knowledge and understanding of metaheuristic algorithms, but has not yet been applied in the domain of operations research
We carried out a meta-analysis of the adaptive layer in adaptive large neighborhood search (ALNS)
Summary
In adaptive large neighborhood search (ALNS), a solution is iteratively destroyed and repaired through the application of several heuristics h ∈ H. The first three table columns list instance names, and the average objective function value across a number of runs of the best solution found by ALNS and its non-adaptive variant, and are obtained from data_individiual_studies_raw.zip. These objective function values are used to calculate the four columns, which evaluate the improvement in the objective function value with the adaptive layer, and whether ALNS outperforms (¬A)LNS or not. A few of the files for some of the individual studies consist of a number of separate sheets, corresponding to the different ALNS versions considered, or for multiple instance classes These results from the individual studies are used for the meta-analysis of the ALNS adaptive layer, available in the table in data_analyzed.xls. The columns in data_analyzed.xls correspond to features obtained for each study, which are used to calculate the importance of the adaptive layer across all studies
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