EconPapers    
Economics at your fingertips  
 

Research on application of optimal particle swarm optimisation algorithm in logistics route improvement

Xianyu Wang

International Journal of Information Technology and Management, 2023, vol. 22, issue 3/4, 301-314

Abstract: Aiming at the logistics path optimisation model, the author converts the logistics path optimisation problem into a classical travelling salesman problem in the field of mathematics. The adaptive particle swarm optimisation algorithm is used to dispose of the model problem. In the algorithm, each particle has four behaviour evolution strategies, and the individual speed and position are updated by selecting the strategy with the highest probability. An adaptive particle swarm optimisation algorithm is proposed. The algorithm improves the speed of individual optimisation by using probabilistic mutation algorithm of policy behaviour, which avoids falling into local optimal solution. For the purpose of demonstrating the effectiveness and performance of the method, comparative experiments are conducted on the open source Oliver30 dataset. Experimental results show that the average path length achieved by the proposed method is closer to the optimal value, and the convergence speed is fast.

Keywords: convergence; particle; swarm; optimisation; multi-strategy; adaptive. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=131816 (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:ids:ijitma:v:22:y:2023:i:3/4:p:301-314

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().

 
Page updated 2025-03-19
Handle: RePEc:ids:ijitma:v:22:y:2023:i:3/4:p:301-314