Abstract
Agricultural machinery rental is a new service form that uses big data in agriculture to improve the utilization rate of agricultural machinery and promote the development of the agricultural economy. To realize agricultural machinery scheduling optimization in cloud services, a dynamic artificial bee-ant colony algorithm (DABAA) is proposed to solve the above problem. First, to improve the practicability of the mathematical model in agricultural production, a dynamic coefficient is proposed. Then the mutation operation is combined with the artificial bee colony (ABC) algorithm to improve the algorithm. Then, iterative threshold adjustment and optimal fusion point evaluation are used to combine the ABC algorithm with the ant colony optimization (ACO) algorithm, which not only improves the search precision but also improves the running speed. Finally, two groups of comparison experiments are carried out, and the results show that the DABAA can obviously improve the running speed and accuracy of cloud services in agricultural machinery rental.
Highlights
The weak purchasing power of agricultural machinery is China’s current condition, and the cost of large agricultural machinery is very high
Artificial intelligence algorithms are introduced in agricultural machinery rental optimization by using agricultural big data, so a dynamic artificial bee-ant colony algorithm (DABAA) is proposed in this paper
Upload of rental orders: The farmers place orders through the client, and the geographical location, expected time, size of farmland, working conditions and other information are uploaded to the cloud service platform for analysis and calculation; Calculation of evaluation indices: The information of each order is transformed into a calculation index to making the scheduling scheme; Determination of the scheduling sequence: After analyzing the evaluation indices of all orders, the optimal scheduling sequence is obtained by using the quality of service (QoS) evaluation model and algorithm
Summary
The weak purchasing power of agricultural machinery is China’s current condition, and the cost of large agricultural machinery is very high. Agricultural machinery rental is a new service form that can lighten the burden of buying agricultural machinery. Agricultural machinery rental services have been combined with big data. Many local small agricultural machinery rental enterprises have started to develop cloud platforms in China. Considering the traveling salesman problem (TSP) and scheduling method, it is still difficult to achieve large-scale agricultural machinery scheduling. Kumar et al proposed a generalized ant colony optimizer algorithm (GACO) to solve the cloud resource allocation problem [14]. Zhou et al realized the optimization and application of agricultural machinery rental in cloud manufacturing services based on the reliability feedback update strategy and the dynamic coefficient strategy [17]. Artificial intelligence algorithms are introduced in agricultural machinery rental optimization by using agricultural big data, so a DABAA is proposed in this paper. To verify the convergence of the DABAA, this paper proves that the algorithm has good performance through experimental simulations and theoretical derivation
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