Local search-based meta-heuristics combined with an improved K-Means++ clustering algorithm for unmanned surface vessel scheduling

Weiyu Tang, Kaizhou Gao, Zhenfang Ma, Zhongjie Lin, Hui Yu, Wuze Huang and Naiqi Wu

International Journal of Production Research, 2025, vol. 63, issue 17, 6339-6363

Abstract: Unmanned surface vessels (USVs) play an important role in marine field, which can improve the efficiency and safety of task execution in hazardous environments. The applications of artificial intelligence technologies on USV collaboration scheduling can guide the USV cluster intelligence. In this study, the scheduling problems of USVs are solved by five local search-based meta-heuristics combining with an improved K-Means++ algorithm. The objective is to minimise the maximum completion time of USVs. For task assignment of USVs, an improved K-Means++ clustering (IKC) algorithm is proposed. The assignment results are used to initialise the population of meta-heuristics. According to the characteristics of the concerned problems and the structure of the solution space, six local search operators are designed to improve the convergence of meta-heuristics. Finally, the proposed strategies are integrated to five meta-heuristics and their performance are verified by solving 40 instances with different scales. Experimental results and statistical tests prove the strong competitiveness of the proposed algorithms. From the statistical analysis, the local search-based harmony search with the IKC algorithm performs better than the compared ones for solving the concerned problems.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2025.2470991 (text/html)
Access to full text is restricted to subscribers.

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:taf:tprsxx:v:63:y:2025:i:17:p:6339-6363

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2025.2470991

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().