Optimal Placement Algorithm for Beehives with Pollination Probability Model
DOI:
https://doi.org/10.54097/6rfc4997Keywords:
Pollination Probability Model, Beehive Covering Model, Voronoi Diagram.Abstract
The beehive placement problem is an important optimization problem widely applied in industrial and logistics domains. This paper proposes pollination probability model and beehive covering model, converting beehive placement problem into spatial partition problem. We perform Voronoi diagram on the farmland based on triangulated beehive points set and define an energy function on each block, decreasing the value of the energy function by continuously moving the hive to the geometric center of each block, iterating until the maximum number of iterations is reached or the maximum moving distance is less than ε. After the iteration, the minimum pollination probability in each block will be calculated. If the probability of a block is less than ξ, the number of beehives will be increased and the algorithm will be re-run until all blocks satisfy the condition. Simply enter the shape of the farmland into the model to get the optimal hive placement, the model works with all common polygonal shapes and can be applied to other similar problems with little modification.
Downloads
References
Vladimir M, Nemanja Z, Jovana J, et al. Modeling a software platform for beehive placement optimization [J]. 2022.
Pan J S, Fu Z, Hu C C, et al. Rafflesia optimization algorithm applied in the logistics distribution centers location problem [J]. Journal of Internet Technology, 2022, 23(7): 1541-1555.
Dou S, Liu G, Yang Y. A new hybrid algorithm for cold chain logistics distribution center location problem [J]. IEEE Access, 2020, 8: 88769-88776.
Samadi M, Nouraei M, Mozaffari M M, et al. Optimal Localization of Shopping Centers Using Metaheuristic Genetic Algorithm [J]. Journal of Optimization in Industrial Engineering, 2020, 13(1): 167-176.
Li H, Xie W, Wen M, et al. An Optimal Location-Allocation Model for Equipment Supporting System Based on Uncertainty Theory [J]. Symmetry, 2023, 15(2): 338.
Cuesta R, Crawford B, Soto R, et al. An artificial bee colony algorithm for the set covering problem[M]//Modern Trends and Techniques in Computer Science. Springer, Cham, 2014: 53-63.
Pasquet R S, Peltier A, Hufford M B, et al. Long-distance pollen flow assessment through evaluation of pollinator foraging range suggests transgene escape distances [J]. Proceedings of the National Academy of Sciences, 2008, 105(36): 13456-13461.
Jan P P. Constrained construction of planar delaunay triangulations without flipping [J]. Structural mechanics of engineering structures and structures, 2018, 14(2): 154-174.
Zheng M, Terzaghi W, Wang H, et al. Integrated strategies for increasing rapeseed yield [J]. Trends in Plant Science, 2022.
Chen J, Messan K, Messan M R, et al. How to model honeybee population dynamics: stage structure and seasonality [J]. arXiv preprint arXiv:2003.09796, 2020.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







