Efficient and Robust Parameter Tuning for Heuristic Algorithms
The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appr...
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| Format: | Article |
| Language: | English |
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Iran University of Science & Technology
2013-06-01
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| Series: | International Journal of Industrial Engineering and Production Research |
| Subjects: | |
| Online Access: | http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-74-1&slc_lang=en&sid=1 |
| _version_ | 1819104927318802432 |
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| author | Hossein Akbaripour Ellips Masehian |
| author_facet | Hossein Akbaripour Ellips Masehian |
| author_sort | Hossein Akbaripour |
| collection | DOAJ |
| description | The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA. |
| first_indexed | 2024-12-22T02:14:08Z |
| format | Article |
| id | doaj.art-ee0e7ef1826d476ebef3ce811dddd61e |
| institution | Directory Open Access Journal |
| issn | 2008-4889 2345-363X |
| language | English |
| last_indexed | 2024-12-22T02:14:08Z |
| publishDate | 2013-06-01 |
| publisher | Iran University of Science & Technology |
| record_format | Article |
| series | International Journal of Industrial Engineering and Production Research |
| spelling | doaj.art-ee0e7ef1826d476ebef3ce811dddd61e2022-12-21T18:42:20ZengIran University of Science & TechnologyInternational Journal of Industrial Engineering and Production Research2008-48892345-363X2013-06-01242143150Efficient and Robust Parameter Tuning for Heuristic AlgorithmsHossein Akbaripour0Ellips Masehian1 Industrial Engineering Department, Tarbiat Modares University, Tehran, Iran Industrial Engineering Department, Tarbiat Modares University, Tehran, Iran The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-74-1&slc_lang=en&sid=1Parameter Tuning Design of Experiments Signal to Noise (S/N) ratio Shannon Entropy VIKOR Simulated Annealing Genetic Algorithms |
| spellingShingle | Hossein Akbaripour Ellips Masehian Efficient and Robust Parameter Tuning for Heuristic Algorithms International Journal of Industrial Engineering and Production Research Parameter Tuning Design of Experiments Signal to Noise (S/N) ratio Shannon Entropy VIKOR Simulated Annealing Genetic Algorithms |
| title | Efficient and Robust Parameter Tuning for Heuristic Algorithms |
| title_full | Efficient and Robust Parameter Tuning for Heuristic Algorithms |
| title_fullStr | Efficient and Robust Parameter Tuning for Heuristic Algorithms |
| title_full_unstemmed | Efficient and Robust Parameter Tuning for Heuristic Algorithms |
| title_short | Efficient and Robust Parameter Tuning for Heuristic Algorithms |
| title_sort | efficient and robust parameter tuning for heuristic algorithms |
| topic | Parameter Tuning Design of Experiments Signal to Noise (S/N) ratio Shannon Entropy VIKOR Simulated Annealing Genetic Algorithms |
| url | http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-74-1&slc_lang=en&sid=1 |
| work_keys_str_mv | AT hosseinakbaripour efficientandrobustparametertuningforheuristicalgorithms AT ellipsmasehian efficientandrobustparametertuningforheuristicalgorithms |