A multi Start adaptive Variable Neighborhood Search metaheuristic for the aircraft landing problem

Abstract

In this paper, a variable neighborhood search metaheuristic is enriched with a multi start technique and an adaptive taboo memory: the SVNS metaheuristic. In the proposed SVNS method, the diversification phase is ameliorated by launching the VNS metaheuristic in multi start technique and by archiving each blocked solution in taboo memory with a three different shames. This taboo memory governs the selection move for the neighborhood. The selection is performed in the neighborhood of each current solution. The SVNS metaheuristic is used to optimize the multiple runways aircraft landing problem. Computational experiments in several problems collected from the literature, instances from OR-Library, demonstrate that the proposed SVNS metaheuristic reaches high-quality solutions using very few user-defined parameters: the Kruskal-Wallis statistic test is used to prove that.