Logistics Distribution Route Optimization Based on Improved Particle Swarm Optimization

Hai Zhao, Ashutosh Sharma


This article improves the logistics distribution route and improves the distribution as well as transportation efficiency. The article combines the features of logistics dissemination along with mathematical designing of dissemination automobile routing issue. The mountain climbing procedure with strong local search ability is introduced into the particle swarm optimization (PSO) procedure to improve the offered approach. Two mountain climbing schemes are offered in this article, and two different hybrid (PSO) procedures are constructed. The experimental outcomes reveals the performance of Hybrid PSO scheme 1 and hybrid PSO scheme 2 offered in this paper which are better than that of standard PSO. Hybrid PSO scheme 2 offers best potential in efficiently solving the routing issue of logistics dissemination automobile. After the issue scale grows, the optimization advantages of Hybrid PSO scheme 2 are fully displayed. It was observed from the experimental analysis that using hybrid PSO scheme 2 to solve the logistics dissemination automobile routing issue can greatly shorten the dissemination mileage.

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Li, H., Yang, D., Su, W., Lü, J., & Yu, X. (2018). An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading. IEEE Transactions on Industrial Electronics, 66(1), 265-275.


Cao, Y., Zhang, H., Li, W., Zhou, M., Zhang, Y., & Chaovalitwongse, W. A. (2018). Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Transactions on Evolutionary Computation, 23(4), 718-731.


Memari, A., Ahmad, R., Rahim, A., Abdul, R., & Hassan, A. (2018). Optimizing a Just-In-Time logistics network problem under fuzzy supply and request: two parameter-tuned metaheuristics algorithms. Neural Computing and Applications, 30(10), 3221-3233.


Son, P. V. H., Duy, N. H. C., & Dat, P. T. (2021). Optimization of construction material cost through logistics planning model of dragonfly algorithm—particle swarm optimization. KSCE Journal of Civil Engineering, 25(7), 2350-2359.


Ha, M. P., Nazari-Heris, M., Mohammadi-Ivatloo, B., & Seyedi, H. (2020). A hybrid genetic particle swarm optimization for distributed generation allocation in power distribution networks. Energy, 209, 118218.


Liu, S., Liang, M., & Hu, X. (2018). Particle swarm optimization inversion of magnetic data: Field examples from iron ore deposits in China. Geophysics, 83(4), J43-J59.


Ahmadian, A., Elkamel, A., & Mazouz, A. (2019). An improved hybrid particle swarm optimization and tabu search algorithm for expansion planning of large dimension electric distribution network. Energies, 12(16), 3052.


Ding, J., Wang, Q., Zhang, Q., Ye, Q., & Ma, Y. (2019). A hybrid particle swarm optimization-cuckoo search algorithm and its engineering applications. Mathematical Problems in Engineering, 2019.


Muhammad, M. H., Mahmoud, K. R., Hameed, M. F. O., & Obayya, S. S. A. (2018). Optimization of highly efficient random grating thin-film solar cell using modified gravitational search algorithm and particle swarm optimization algorithm. Journal of Nanophotonics, 12(1), 016016.


Wang, Y., Assogba, K., Fan, J., Xu, M., Liu, Y., & Wang, H. (2019). Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost. Journal of Cleaner Production, 232, 12-29.


Yuan, F., Lv, K., Tang, B., Wang, Y., Yang, W., Qin, S., & Ding, C. (2021). Optimization design of oil-immersed iron core reactor based on the particle swarm algorithm and thermal network model. Mathematical problems in Engineering, 2021.


Moosavian, N., & Lence, B. (2019). Testing evolutionary algorithms for optimization of water distribution networks. Canadian Journal of Civil Engineering, 46(5), 391-402.


Lagos, C., Guerrero, G., Cabrera, E., Moltedo, A., Johnson, F., & Paredes, F. (2018). An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Latin America Transactions, 16(6), 1732-1740.


Wan, M., Gu, G., Qian, W., Ren, K., Chen, Q., & Maldague, X. (2018). Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement. Infrared Physics & Technology, 91, 164-181.


Li, S., Zhang, Q., Zhang, Z., Zhao, Q., & Liang, L. (2021). Improved subgroup method coupled with particle swarm optimization algorithm for intra-pellet non-uniform temperature distribution problem. Annals of Nuclear Energy, 153, 108070.


Sun, S. H., Yu, T. T., Nguyen, T. T., Atroshchenko, E., & Bui, T. Q. (2018). Structural shape optimization by IGABEM and particle swarm optimization algorithm. Engineering Analysis with Boundary Elements, 88, 26-40.


Ceylan, O. (2021). Multi-verse optimization algorithm-and salp swarm optimization algorithm-based optimization of multilevel inverters. Neural Computing and Applications, 33(6), 1935-1950.


Wang, Y., Assogba, K., Liu, Y., Ma, X., Xu, M., & Wang, Y. (2018). Two-echelon location-routing optimization with time windows based on customer clustering. Expert Systems with Applications, 104, 244-260.


Silva, L. I., Belati, E. A., Gerez, C., & Silva Junior, I. C. (2021). Reduced search space combined with particle swarm optimization for distribution system reconfiguration. Electrical Engineering, 103(2), 1127-1139.


Chen, J., & Shi, J. (2019). A multi-compartment vehicle routing problem with time windows for urban distribution–A comparison study on particle swarm optimization algorithms. Computers & Industrial Engineering, 133, 95-106.


DOI: https://doi.org/10.31449/inf.v47i2.4011

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