Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP
Travelling Salesman Problem (TSP) is a traveling salesman optimization problems in visiting the city and every town just skipped right one. Tsp problem can be applied to various activities are to optimize, in the completion of TSP there are several methods that can be used, including the algorithms...
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| Format: | Article |
| Language: | English |
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University of Serambi Mekkah
2017-12-01
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| Series: | Jurnal Serambi Engineering |
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| Online Access: | https://ojs.serambimekkah.ac.id/jse/article/view/323 |
| _version_ | 1811206871497834496 |
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| author | Munawir, Taufik Abdul Gani |
| author_facet | Munawir, Taufik Abdul Gani |
| author_sort | Munawir, Taufik Abdul Gani |
| collection | DOAJ |
| description | Travelling Salesman Problem (TSP) is a traveling salesman optimization problems in visiting the city and every town just skipped right one. Tsp problem can be applied to various activities are to optimize, in the completion of TSP there are several methods that can be used, including the algorithms evolve. To increase diversity and raise the quality of the solution, the method used is the replacement strategy. This study analyzes the replacement method of steady state and generational strategy. Replacement strategy steady state will be trapped local optimum because of the new individual created only one new member to be tested for inclusion in the population further, while the replacement strategy generational diversity will increase as generational this procedure replaces all individuals in a generation is replaced at once by a number of individuals The new results of crossover and mutation. In this study, the test data used is datatsp lib as much as 5 dataset, and raised as much as 128 generations, the testing of each data set 10 times of testing, resulting from this test is the average minimum distance and diversity, after testing then get a conclusion that by using the merger method of replacement strategy generational steady state and the shortest distance to get a solution that is more optimal. |
| first_indexed | 2024-04-12T03:54:09Z |
| format | Article |
| id | doaj.art-96c27bfab4494b14bf28a476fbe81760 |
| institution | Directory Open Access Journal |
| issn | 2528-3561 2541-1934 |
| language | English |
| last_indexed | 2024-04-12T03:54:09Z |
| publishDate | 2017-12-01 |
| publisher | University of Serambi Mekkah |
| record_format | Article |
| series | Jurnal Serambi Engineering |
| spelling | doaj.art-96c27bfab4494b14bf28a476fbe817602022-12-22T03:48:53ZengUniversity of Serambi MekkahJurnal Serambi Engineering2528-35612541-19342017-12-012110.32672/jse.v2i1.323305Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSPMunawir, Taufik Abdul Gani0Prodi Teknik Informatika, Fakultas Teknik Universitas Serambi MekkahTravelling Salesman Problem (TSP) is a traveling salesman optimization problems in visiting the city and every town just skipped right one. Tsp problem can be applied to various activities are to optimize, in the completion of TSP there are several methods that can be used, including the algorithms evolve. To increase diversity and raise the quality of the solution, the method used is the replacement strategy. This study analyzes the replacement method of steady state and generational strategy. Replacement strategy steady state will be trapped local optimum because of the new individual created only one new member to be tested for inclusion in the population further, while the replacement strategy generational diversity will increase as generational this procedure replaces all individuals in a generation is replaced at once by a number of individuals The new results of crossover and mutation. In this study, the test data used is datatsp lib as much as 5 dataset, and raised as much as 128 generations, the testing of each data set 10 times of testing, resulting from this test is the average minimum distance and diversity, after testing then get a conclusion that by using the merger method of replacement strategy generational steady state and the shortest distance to get a solution that is more optimal.https://ojs.serambimekkah.ac.id/jse/article/view/323replacement strategy, steady state, generational, algorithms evolve |
| spellingShingle | Munawir, Taufik Abdul Gani Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP Jurnal Serambi Engineering replacement strategy, steady state, generational, algorithms evolve |
| title | Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP |
| title_full | Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP |
| title_fullStr | Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP |
| title_full_unstemmed | Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP |
| title_short | Penggabungan Metode Replacement Strategy Steady State dan Generational Dalam Algoritma Berevolusi untuk Penyelesaian TSP |
| title_sort | penggabungan metode replacement strategy steady state dan generational dalam algoritma berevolusi untuk penyelesaian tsp |
| topic | replacement strategy, steady state, generational, algorithms evolve |
| url | https://ojs.serambimekkah.ac.id/jse/article/view/323 |
| work_keys_str_mv | AT munawirtaufikabdulgani penggabunganmetodereplacementstrategysteadystatedangenerationaldalamalgoritmaberevolusiuntukpenyelesaiantsp |