Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Juan Li, Dan-dan Xiao, Hong Lei, Ting Zhang and Tian Tian
Additional contact information
Juan Li: School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China
Dan-dan Xiao: School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China
Hong Lei: School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
Ting Zhang: School of Information Engineering, Wuhan Technology and Business University, Wuhan 430065, China
Tian Tian: School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China

Mathematics, 2020, vol. 8, issue 2, 1-32

Abstract: Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q -Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

Keywords: global optimization; cuckoo search algorithm; Q- learning; mutation; self-adaptive step size (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/2/149/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/2/149/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:2:p:149-:d:311424

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().