A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA
The problem of locating naval platforms in the operation region with the aim of maximizing both total radar coverage and critical radar coverage is solved by using Multiobjective Evolutionary Algorithms (MOEA). Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and S-Metric Selection Evolutionary...
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| Format: | Article |
| Language: | English |
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Pamukkale University
2018-02-01
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| Series: | Pamukkale University Journal of Engineering Sciences |
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| Online Access: | https://dergipark.org.tr/tr/pub/pajes/issue/35876/400819 |
| _version_ | 1828028624203153408 |
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| author | Ertan Yakıcı |
| author_facet | Ertan Yakıcı |
| author_sort | Ertan Yakıcı |
| collection | DOAJ |
| description | The
problem of locating naval platforms in the operation region with the aim of
maximizing both total radar coverage and critical radar coverage is solved by
using Multiobjective Evolutionary Algorithms (MOEA). Non-Dominated Sorting
Genetic Algorithm-II (NSGA-II) and
S-Metric Selection Evolutionary Multiobjective Optimization Algorithm
(SMS-EMOA) procedures are implemented. Experiments show that evolutionary
algorithms provide good and diverse alternatives that are considered to be very
close to Pareto-optimal front. The performances of NSGA-II and SMS-EMOA
approaches are compared employing the hypervolume indicator technique. The
performance of NSGA-II is found better in terms of both convergence and
diversity |
| first_indexed | 2024-04-10T13:55:18Z |
| format | Article |
| id | doaj.art-66b638add6c44fea91bc971efbf64669 |
| institution | Directory Open Access Journal |
| issn | 1300-7009 2147-5881 |
| language | English |
| last_indexed | 2024-04-10T13:55:18Z |
| publishDate | 2018-02-01 |
| publisher | Pamukkale University |
| record_format | Article |
| series | Pamukkale University Journal of Engineering Sciences |
| spelling | doaj.art-66b638add6c44fea91bc971efbf646692023-02-15T16:10:29ZengPamukkale UniversityPamukkale University Journal of Engineering Sciences1300-70092147-58812018-02-0124194100218A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOAErtan YakıcıThe problem of locating naval platforms in the operation region with the aim of maximizing both total radar coverage and critical radar coverage is solved by using Multiobjective Evolutionary Algorithms (MOEA). Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and S-Metric Selection Evolutionary Multiobjective Optimization Algorithm (SMS-EMOA) procedures are implemented. Experiments show that evolutionary algorithms provide good and diverse alternatives that are considered to be very close to Pareto-optimal front. The performances of NSGA-II and SMS-EMOA approaches are compared employing the hypervolume indicator technique. The performance of NSGA-II is found better in terms of both convergence and diversityhttps://dergipark.org.tr/tr/pub/pajes/issue/35876/400819fleet locationoptimal sensor placementmultiobjective evolutionary algorithmsfilo konumlandırmaoptimal sensör yerleşimiçok amaçlı evrimsel algoritmalar |
| spellingShingle | Ertan Yakıcı A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA Pamukkale University Journal of Engineering Sciences fleet location optimal sensor placement multiobjective evolutionary algorithms filo konumlandırma optimal sensör yerleşimi çok amaçlı evrimsel algoritmalar |
| title | A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA |
| title_full | A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA |
| title_fullStr | A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA |
| title_full_unstemmed | A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA |
| title_short | A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA |
| title_sort | multiobjective fleet location problem solved by adaptation of evolutionary algorithms nsga ii and sms emoa |
| topic | fleet location optimal sensor placement multiobjective evolutionary algorithms filo konumlandırma optimal sensör yerleşimi çok amaçlı evrimsel algoritmalar |
| url | https://dergipark.org.tr/tr/pub/pajes/issue/35876/400819 |
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