Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm
As a novel swarm intelligence optimization algorithm, cuckoo search (CS), has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. Although a lot of research has b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-12-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://www.atlantis-press.com/article/125949974/view |
| _version_ | 1828748213789655040 |
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| author | Juan Li Yuan-Hua Yang Hong Lei Gai-Ge Wang |
| author_facet | Juan Li Yuan-Hua Yang Hong Lei Gai-Ge Wang |
| author_sort | Juan Li |
| collection | DOAJ |
| description | As a novel swarm intelligence optimization algorithm, cuckoo search (CS), has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. Although a lot of research has been done on the shortage of CS, learning mechanism has not been used to achieve the balance between exploitation and exploration. Based on this, a differential CS extension with balanced learning namely Cuckoo search algorithm with balanced-learning (O-BLM-CS) is proposed. Two sets, the better fitness set (FSL) and the better diversity set (DSL), are produced in the iterative process. Two excellent individuals are selected from two sets to participate in search process. The search ability is improved by learning their beneficial behaviors. The FSL and DSL learning factors are adaptively adjusted according to the individual at each generation, which improve the global search ability and search accuracy of the algorithm and effectively balance the contradiction between exploitation and exploration. The performance of O-BLM-CS algorithm is evaluated through eighteen benchmark functions with different characteristics and the logistics distribution center location problem. The results show that O-BLM-CS algorithm can achieve better balance between exploitation and exploration than other improved CS algorithms. It has strong competitiveness in solving both continuous and discrete optimization problems. |
| first_indexed | 2024-04-14T04:57:56Z |
| format | Article |
| id | doaj.art-74005029ae2340b3b949b384050bd70b |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-14T04:57:56Z |
| publishDate | 2020-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-74005029ae2340b3b949b384050bd70b2022-12-22T02:11:06ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832020-12-0114110.2991/ijcis.d.201216.002Solving Logistics Distribution Center Location with Improved Cuckoo Search AlgorithmJuan LiYuan-Hua YangHong LeiGai-Ge WangAs a novel swarm intelligence optimization algorithm, cuckoo search (CS), has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. Although a lot of research has been done on the shortage of CS, learning mechanism has not been used to achieve the balance between exploitation and exploration. Based on this, a differential CS extension with balanced learning namely Cuckoo search algorithm with balanced-learning (O-BLM-CS) is proposed. Two sets, the better fitness set (FSL) and the better diversity set (DSL), are produced in the iterative process. Two excellent individuals are selected from two sets to participate in search process. The search ability is improved by learning their beneficial behaviors. The FSL and DSL learning factors are adaptively adjusted according to the individual at each generation, which improve the global search ability and search accuracy of the algorithm and effectively balance the contradiction between exploitation and exploration. The performance of O-BLM-CS algorithm is evaluated through eighteen benchmark functions with different characteristics and the logistics distribution center location problem. The results show that O-BLM-CS algorithm can achieve better balance between exploitation and exploration than other improved CS algorithms. It has strong competitiveness in solving both continuous and discrete optimization problems.https://www.atlantis-press.com/article/125949974/viewCuckoo search algorithmBalanced-learningThe better fitness setThe better diversity setOptimization algorithm |
| spellingShingle | Juan Li Yuan-Hua Yang Hong Lei Gai-Ge Wang Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm International Journal of Computational Intelligence Systems Cuckoo search algorithm Balanced-learning The better fitness set The better diversity set Optimization algorithm |
| title | Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm |
| title_full | Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm |
| title_fullStr | Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm |
| title_full_unstemmed | Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm |
| title_short | Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm |
| title_sort | solving logistics distribution center location with improved cuckoo search algorithm |
| topic | Cuckoo search algorithm Balanced-learning The better fitness set The better diversity set Optimization algorithm |
| url | https://www.atlantis-press.com/article/125949974/view |
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