International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 676 - 692

Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm

Authors
Juan Li1, 2, 3, Yuan-Hua Yang1, Hong Lei2, Gai-Ge Wang4, 5, *
1School of Computer and Information Engineering, Hubei Normal University, Huangshi 435002, China
2School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
3Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
4Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China
5Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China
*Corresponding author. Email: gaigewang@163.com
Corresponding Author
Gai-Ge Wang
Received 15 August 2020, Accepted 8 December 2020, Available Online 28 December 2020.
DOI
10.2991/ijcis.d.201216.002How to use a DOI?
Keywords
Cuckoo search algorithm; Balanced-learning; The better fitness set; The better diversity set; Optimization algorithm
Abstract

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.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
676 - 692
Publication Date
2020/12/28
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.201216.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Juan Li
AU  - Yuan-Hua Yang
AU  - Hong Lei
AU  - Gai-Ge Wang
PY  - 2020
DA  - 2020/12/28
TI  - Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 676
EP  - 692
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201216.002
DO  - 10.2991/ijcis.d.201216.002
ID  - Li2020
ER  -