Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.
A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0271928 |
| _version_ | 1817995240106098688 |
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| author | Tsung-Xian Lin Zhong-Huan Wu Wen-Tsao Pan |
| author_facet | Tsung-Xian Lin Zhong-Huan Wu Wen-Tsao Pan |
| author_sort | Tsung-Xian Lin |
| collection | DOAJ |
| description | A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future. |
| first_indexed | 2024-04-14T02:03:06Z |
| format | Article |
| id | doaj.art-f42a857e11974c02904048e86271986e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-14T02:03:06Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f42a857e11974c02904048e86271986e2022-12-22T02:18:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027192810.1371/journal.pone.0271928Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.Tsung-Xian LinZhong-Huan WuWen-Tsao PanA clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.https://doi.org/10.1371/journal.pone.0271928 |
| spellingShingle | Tsung-Xian Lin Zhong-Huan Wu Wen-Tsao Pan Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. PLoS ONE |
| title | Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. |
| title_full | Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. |
| title_fullStr | Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. |
| title_full_unstemmed | Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. |
| title_short | Optimal location of logistics distribution centres with swarm intelligent clustering algorithms. |
| title_sort | optimal location of logistics distribution centres with swarm intelligent clustering algorithms |
| url | https://doi.org/10.1371/journal.pone.0271928 |
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