DEVELOPMENT OF INNOVATIVE APPROACHES FOR NETWORK OPTIMIZATION USING GEOSPATIAL MULTI-COMPONENT SYSTEMS
DOI:
https://doi.org/10.15588/1607-3274-2025-2-16Keywords:
geospatial multi-agent system, optimization of transportation networks, evolution strategyAbstract
Context. Developing a geospatial multi-agent system for optimizing transportation networks is crucial for enhancing efficiency and reducing travel time. This involves employing optimization algorithms and simulating agent behavior within the network.
Objective. The aim of this study is to develop a geospatial multi-agent system for optimizing transportation networks, focusing on improving network efficiency and minimizing travel time through the application of advanced optimization algorithms and agentbased modeling.
Method. The proposed method for optimizing transportation networks combines foundational structure with advanced refinement in two stages: pre-processing and evolutionary strategy optimization. In the first stage, a Minimum Spanning Tree is constructed using Kruskal’s algorithm to establish the shortest, loop-free network that connects all key points, accounting for natural obstacles and existing routes. This provides a cost-effective and realistic baseline. The second stage refines the network through an evolutionary strategy, where agents representing MST variations are optimized using a fitness function balancing total path length, average node distances, and penalties for excessive edges. Optimization employs crossover to combine solutions and mutation to introduce diversity through edge modifications. Repeated over multiple epochs, this process incrementally improves the network, resulting in an optimized design that minimizes costs, enhances connectivity, and respects real-world constraints.
Results. The results of applying the evolutionary strategy and minimum spanning tree methods were analyzed in detail. Comparing these methods to benchmarks like Tokyo’s railway network and the Slime Mold algorithm revealed the advantage of using the evolutionary approach in generating optimal paths. The findings emphasize the need for integrating advanced algorithms to further refine path optimization and network design.
Conclusions. The research successfully developed a geospatial multi-agent system for optimizing transportation networks, achieving its objectives by addressing key challenges in transport network planning. A detailed analysis of existing solutions revealed the dynamic and complex nature of transportation systems and underscored the need for adaptability to environmental changes, such as new routes or obstacles. The proposed approach enhanced the minimum spanning tree with an evolutionary strategy, enabling flexibility and rapid adaptation. Results demonstrated the system’s effectiveness in planning optimal intercity transport networks. Future work could refine environmental assessments, improve route cost evaluations, expand metrics, define new performance criteria, and integrate neural network models to further enhance optimization capabilities, particularly for urban networks.
References
Moher D., Liberati A., Tetzlaff J. Preferred Reporting Items for Systematic Reviews and Meta-Analyses, The PRISMA Statement. PLoS Medicine, 2020, Vol. 6(7), P. e1000097, Mode of access: https://doi.org/10.1371/journal.pmed.1000097.
Oishi K., Hashizume Y., Jimbo T., Kaji H., Kashima K. Imitation-regularized optimal transport on networks: provable robustness and application to logistics planning, arXiv preprint arXiv, 2024, P. 1906.07114, Mode of access: https://doi.org/10.48550/arXiv.2402.17967.
Andrecut M. Heuristic Optimal Transport in Branching Networks, arXiv preprint arXiv, 2021, P. 1906.07115, Mode of access: https://doi.org/10.48550/arXiv.2311.06650.
Cominetti L., Como G., Ozdaglar A., Parise F. Optimal intervention in traffic networks, arXiv preprint arXiv, 2021, P. 1906.07116, Mode of access: https://doi.org/10.48550/arXiv.2102.08441.
Wu J., Pu C., Ding S., Cao G., Pardalos P. M. NC-MOPSO: Network centrality guided multi-objective particle swarm optimization for transport optimization on networks, arXiv preprint arXiv, 2021, P. 906.07117, Mode of access: https://doi.org/10.48550/arXiv.2009.03575.
Aslani B., Mohebbi S. Learn to decompose multiobjective optimization models for large-scale networks, arXiv preprint arXiv, 2021, P. 1906.07118, Mode of access: https://doi.org/10.1111/itor.13169.
Sbayti O., Housni K., Hicham Hanin M., El Makrani A. Comparative analysis of proactive and reactive routing protocols in VANET environment, International Journal of Electrical and Computer Engineering (IJECE), 2023, Vol. 13(5), P. 5374–5387, Mode of access: https://doi.org/10.11591/ijece.v13i5.pp5374-5387.
Rafael H., Pereira M., Herszenhut D., Saraiva M., Farber S. Ride-hailing and transit accessibility considering the trade-off between time and money, Cities, 2024, Vol. 144, P. 104663, Mode of access: https://doi.org/10.1016/j.cities.2023.104663
Baptista D., De Bacco C. Convergence properties of optimal transport-based temporal networks, arXiv preprint arXiv, 2021, Mode of access: https://doi.org/10.1007/978-3-030-93409-5_48
Wen W., Zhang W., Deng H. Research on Urban Road Network Evaluation Based on Fractal Analysis, Journal of Advanced Transportation, 2023, P. 1–8, Mode of access: https://doi.org/10.1155/2023/9938001.
Choi J., Kang M. Analyzing and Improving Optimal-Transportbased Adversarial Networks, arXiv:2310.02611, 2023, Mode of access: https://doi.org/10.48550/arXiv.2310.02611.
VANET (Vehicular Ad-hoc Network) [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/Vehicular_adhoc_network (access date: 11.12.2024). Title from the screen.
AODV (Ad-hoc On-Demand Distance Vector routing) [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/Ad-hoc_On- Demand_Distance_Vector_routing (access date: 11.12.2024). Title from the screen.
DSDV (Destination-Sequenced Distance Vector routing) [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/Destination-Sequenced_Distance_Vector_routing (access date: 11.12.2024). Title from the screen.
OLSR (Optimized Link State Routing) [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/Optimized_Link_State_Routing_Protocol (access date: 11.12.2024). Title from the screen.
Yang X.-S. Nature-Inspired Algorithms in Optimization: Introduction, Hybridization and Insights, arXiv:2401.00976, 2023, Mode of access: https://doi.org/10.1007/978-981-99-3970-1.
Wei Y., Othman Z., Mohd Daud K., Luo Q., Zhou Y. Advances in Slime Mould Algorithm: A Comprehensive Survey, Biomimetics, 2024, Vol. 9, P. 31, Mode of access: https://doi.org/10.3390/biomimetics9010031.
Bellman-Ford Algorithm [Electronic resource]. Access mode: https://brilliant.org/wiki/bellman-ford-algorithm (access date: 11.12.2024). Title from the screen.
A* Algorithm [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/A*_search_algorithm (access date: 11.12.2024). Title from the screen.
Network flow problem [Electronic resource]. Access mode: https://en.wikipedia.org/wiki/Network_flow_problem (access date: 11.12.2024). Title from the screen.
Network flow problem, Ford–Fulkerson algorithm [Electronic resource]. Access mode: https://www.networkflowalgs.com/book.pdf (access date: 11.12.2024). Title from the screen.
Wagdy A., Hadi A. A, Khater A. Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm, International Journal of Machine Learning and Cybernetics, 2023, Mode of access: https://doi.org/10.1007/s13042-019-01053-x.
SlimeMould [Electronic resource]. Access mode: https://github.com/MoeBuTa/SlimeMould (access date: 11.12.2024). Title from the screen.
Boyko N., Pytel A. Aspects of the study of genetic algorithms and mechanisms for their optimization for the travelling salesman problem, International Journal of Computing, 2021, Vol. 20(4), P. 543–550, Mode of access: https://doi.org/10.47839/ijc.20.4.2442.
Vyklyuk Ya., Nevinskyi D., Boyko N. GeoCity – a New Dynamic-Spatial Model of Urban Ecosystem, J. Geogr. Inst. Cvijic, 2023, Vol. 73(2), P. 187–203, Mode of access:
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