The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance
Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ per...
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doaj-dd8cdcc497474d44b203a4dbc2cc50e62020-11-24T23:11:33ZengHindawi LimitedJournal of Mathematics2314-46292314-47852018-01-01201810.1155/2018/80726218072621The Influence of Problem Specific Neighborhood Structures in Metaheuristics PerformanceA. S. Santos0A. M. Madureira1M. L. R. Varela2Interdisciplinary Studies Research Center (ISRC), School of Engineering, Polytechnic Institute of Porto (ISEP/IPP), PortugalInterdisciplinary Studies Research Center (ISRC), School of Engineering, Polytechnic Institute of Porto (ISEP/IPP), PortugalDepartment of Production and Systems, University of Minho, PortugalMetaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement.http://dx.doi.org/10.1155/2018/8072621 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. S. Santos A. M. Madureira M. L. R. Varela |
spellingShingle |
A. S. Santos A. M. Madureira M. L. R. Varela The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance Journal of Mathematics |
author_facet |
A. S. Santos A. M. Madureira M. L. R. Varela |
author_sort |
A. S. Santos |
title |
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance |
title_short |
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance |
title_full |
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance |
title_fullStr |
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance |
title_full_unstemmed |
The Influence of Problem Specific Neighborhood Structures in Metaheuristics Performance |
title_sort |
influence of problem specific neighborhood structures in metaheuristics performance |
publisher |
Hindawi Limited |
series |
Journal of Mathematics |
issn |
2314-4629 2314-4785 |
publishDate |
2018-01-01 |
description |
Metaheuristics (MH) aptitude to move past local optimums makes them an attractive technique to approach complex computational problems, such as the Travelling Salesman Problem (TSP), but there is lack of information on the parameterization procedure and the appropriate parameters to improve MHs’ performance. In this paper the parameterization procedure of Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) is addressed, with a focus on the Neighborhood Structure (NS). Numerous NS have been proposed for specific problems, which seem to indicate that the NS is a special parameter, whose optimization is independent of other parameters. The performance of eight NS was examined with SA and DABC under two optimization constraints, regarding computational time variation, to determine if there is one appropriate NS for the TSP problem, independent of the rest of the parameters of the optimization procedure. The computational study carried out for comparing the evaluation of the NS, including a statistical analysis, demonstrated a nonproportional increase in the performance of DABC with some NS. For SA the improvement of the solutions appeared to be more uniform with an almost nonexistent variance in improvement. |
url |
http://dx.doi.org/10.1155/2018/8072621 |
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