Optimization of electric vehicle distribution routes for multiple distribution centers based on biomechanic principles and improved Plant Growth Simulation Algorithm (PGSA)
Abstract
This study focuses on the optimization of electric vehicle delivery routes for multiple distribution centers, proposing a dynamic route optimization model based on an improved Plant Growth Simulation Algorithm (PGSA). Inspired by the growth mechanisms of plants in nature, PGSA simulates the growth behavior of plants under light and resource distribution. According to the knowledge of molecular and cellular biomechanics, the growth process of plants can be seen as a series of mechanical and biological responses. By simulating this growth behavior, PGSA optimizes path selection through phototropism and resource acquisition, providing novel insights for the design of electric vehicle delivery routes. This paper enhances PGSA by introducing a variable step-size search mechanism, simulating the pattern of plant branches growing from long to short, gradually narrowing the search scope to improve search efficiency. Simultaneously, it randomly rearranges auxin concentration to mimic the dynamic changes in hormone concentration at plant growth points, enhancing search diversity and avoiding local optima. Through simulation experiments, the improved PGSA significantly reduces computation time and iteration counts when solving large-scale dynamic route optimization problems for multiple distribution centers, offering an efficient and intelligent solution for electric vehicle delivery route optimization. By integrating biological principles with optimization algorithms, this study not only expands the application domain of PGSA but also lays the foundation for further research on bio-inspired algorithms in logistics optimization.
References
1. Zhang Y, Zhu P. Research on site selectionmodel of special charging stations for taxis. Journal of Geo-Information Science. 2021; 23(5): 802–811.
2. Chen G, Dong Y, Liang Z. Analysis and reflection on high-quality developmentof new energy with Chinese characteristics in energytransition. Proceedings of the CSEE. 2020; 40(17): 5493–5506.
3. Dantzig GB, Ramser JH. The truck dispatching problem. Management Science. 1959; 6(1): 80–91.
4. Teng Y, Sun L, Zhou X. Heterogeneous Fleet Vehicle Routing Problem Considering Transportation Risk of Hazardous Materials. Systems Engineering. 2020; 38(01): 93–102.
5. Zhou Y, Su Y, Xie A, Kong L. A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV. Chinese Journal of Aeronautics. 2021; 34(9): 199–209.
6. Wang T, Ni J. Improved Intelligent Water Droplet Algorithms for Solving Vehicle Routing Problem with Mixed Time Windows. Science and Technology Management Research. 2019; 39(11): 246–253.
7. Han JJ, Li YX. Overview on Dynamic Vehicle Routing Problem. Journal of Green Science and Technology. 2015; (05): 285–288.
8. Zhou X, Wang L, Zhou KJ, Huang X. Research progress and development trend of dynamic vehicle routing problem. Control and Decision. 2019; 34(03): 449–458.
9. Zhang T, Lai PZ, He QF, Jin ZH. Optimization of Dynamic Vehicle Routing of Urban Distribution Based on Real-time Information. System Engineering. 2015; 33(07): 58–64.
10. Chen PY, Zhang YB. Multi-depot Vehicle Routing Problem Based on Immune Clonal Selection Algorithm. Science and Technology Management Research. 2013; 33(03): 213–218.
11. Xiao Y, Liu Z, Lou X. Study on collaborative distribution problem of multi-distribution with time windows. Mathematics in Practice and Theory. 2018; 48(14): 171–177.
12. Xiao Z, Tan J, Zhou Y, Zeng J. Research on intelligent optimization of trans-regional vehicle scheduling among multiple distribution with time windows. Acta Tabacaria Sinica. 2017; 23(04): 114–120.
13. Shi CC, Wang X, Ge XL. Research on Vehicle Scheduling Problem of Multi-distribution depots with time window. Computer Engineering and Applications. 2009; 45(34): 21–24.
14. Yin Z, Ye CM. Study on the Hierarchical Model for Multi-depot Logistic Vehicle Scheduling Problem. Journal of Systems & Management. 2014; 23(04): 602–606.
15. Ge X, Xu M, Wang W. Route optimization of urban logistics in joint distribution. Control and decision. 2016; 31(03): 503–512.
16. Li T, Wang CF, Wang WB, et al. A Global Optimization Bionics Algorithm for Solving Integer Programming-Plant Growth Simulation Algorithm. Systems Engineering-Theory & Practice. 2005; 25(1): 76–85.
17. Wu D, Wang N, Song N, Wu N. The optimization for location inventory routing problem of remote islands logistic system. Systems Engineering-Theory& Practice. 2016; 36(12): 3175–3187.
18. Ma S, Yang J. Resource allocation optimization based on modular manufacturing cells considering different processing ability of homogeneous machines. Control and Decision. 2015; 30(03): 410–416.
19. Shi Z, Fu Z. Distribution Location-routing Problem of Heterotypic Vehicle and its Algorithm. Computer Science. 2015; 42(5): 245–250.
20. Wang XP, Zhang NN, Zhan HX. Emergency Material Allocation Model Considering the Non-Rational Psychological Comparison of the Victims. Chinese Journal of Management. 2016; 13(7): 1075–1080.
21. Injeti SK, Thunuguntla VK, Shareef M. Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms. International Journal of Electrical Power & Energy Systems. 2015; 69: 441–455.
22. Li T, Chen CY. Plant Growth Simulation Algorithm for Solving Nonlinear Bi-level Programming. Chinese Journal of Management Science. 2012; 20(4): 160–166.
23. Yan F, Wang YY. Modeling and Solving the Vehicle Routing Problem with Multiple Fuzzy Time Windows. Journal of Transportation Systems Engineering and Information Technology. 2016; 16(6): 182–188.
24. Ma X, Fu B. Research of Reactive Power Optimization in Distribution System with Wind Power Generation. Henan Electric Power. 2015; 43(04): 30–34.
25. Luo WQ, Yu JT, Huang JD. A Biomimetic Algorithm for Finding the Optimal Solution of Nonlinear Integer Programming. Computer Engineer and Applications. 2008; 44(7): 57–59.
26. Li Tong, Wang Zhongtuo. Principles and Applications of Plant Growth Simulation Algorithm[J]. Systems Engineering—Theory & Practice, 2020, 40(05): 1266–1280.
27. Xu L. A Review of Vehicle Scheduling Applications Based on Plant Growth Simulation Algorithm. China Storage & Transport. 2021; (06): 77–78.
28. Yang Y, Yang Q, An W, et al. Nonlinear Fusion Method for Multistage Product Design Decision-Making Using Plant Growth Simulation Algorithm. Advanced Engineering Informatics. 2022; 53: 101712.
29. Meng X, Wang Y, Qin Y, et al. Railway Transit Network Design Based on Fuzzy Plant Growth Simulation Algorithm. Transportation Letters. 2022; 14(2): 186–194.
30. Bai X, Li B, Xu X, et al. USV Path Planning Algorithm Based on Plant Growth. Ocean Engineering. 2023; 273: 113965.
31. Ghasemi M, Mohsen Z, Pavel T, et al. Optimization Based on the Smart Behavior of Plants with Its Engineering Applications: Ivy Algorithm. Knowledge-Based Systems. 2024; 295: 111850.
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
