The agricultural Internet of Things system, with its large-scale, highly heterogeneous, and dynamic characteristics, brings certain difficulties to the provision of agricultural Internet of Things services. Considering the multiple requests of the agricultural Internet of Things at any random moment, which have the characteristics of multiple sources, multiple types, and uneven tasks, this paper establishes an optimization model for the minimum service cost and proposes a collaborative evolution to intelligent agricultural dynamic services under the Internet of Things environment multiobjective optimization method. First, according to the probability that the allele on the fragment to be vaccinated has appeared in the memory bank, use the detection strategy to judge whether the solution is illegal; secondly, compare the optimal individual with other values appearing on the gene locus, judge whether the optimal gene or fall into the local optimal, and inoculate with probability through simulated annealing; finally, the total service cost and service time were evaluated under the two service provision strategies and compared with the other three intelligent algorithms; the results confirmed the feasibility and effectiveness of the proposed algorithm. At the same time, the simulation results show that the proposed collaborative multiobjective optimization algorithm can achieve better performance.
Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture
