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Title: A Double-Adaptive General Variable Neighborhood Search algorithm for the solution of the Traveling Salesman Problem
Authors: Karakostas, Panagiotis
Sifaleras, Angelo
Type: Article
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Variable Neighborhood Search
Adaptive search
Intelligent Optimization
Traveling Salesman Problem
Issue Date: 28-Mar-2022
Publisher: Elsevier
Source: Applied Soft Computing
First Page: 108746
Abstract: This work addresses a novel General Variable Neighborhood Search (GVNS) solution method, which integrates intelligent adaptive mechanisms to re-order the search operators during the intensification and diversification phases, in an effort to enhance its overall efficiency. To evaluate the performance of the new GVNS scheme, asymmetric and symmetric instances of the classic Traveling Salesman Problem (TSP) from the TSPLib were solved. The obtained results of the Double-Adaptive GVNS were compared with those achieved by two single-adaptive GVNS, which use an adaptive mechanism either for the intensification or the diversification phase and by a conventional GVNS. For a fair comparison, all GVNS schemes were structured using the same local search and shaking operators. Moreover, the novel GVNS algorithm was compared with some recent solutions methods for the TSP, found in the open literature. The comparative studies revealed the high efficiency of the novel VNS scheme and underlines the significant impact of intelligent mechanisms on the performance of classic metaheuristic frameworks.
ISSN: 1568-4946
Other Identifiers: 10.1016/j.asoc.2022.108746
Appears in Collections:Department of Applied Informatics

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