Research on Agricultural Machinery Rental Optimization Based on the Dynamic Artificial Bee-Ant Colony Algorithm

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.

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

“…The value was higher than the max-min ant system (MMAS), artificial bee colony (ABC), and the genetic algorithm. The observations drawn from the latter study affirm the superiority of the ant colony algorithm [139]. The positive observations drawn from the two research studies yielded compelling evidence that the ant colony algorithm was an effective and practical metaheuristic algorithm.…”

“…The evidence drawn from the ZhuoZhou experimental farm was corroborated by Bakhtiari et al, who noted that the ant colony algorithm-based B-patterns minimizes operational costs (fuel consumed by agricultural equipment, non-working distance traveled, and operational time) [136]. The cost savings reported by Bakhtiari et al [136] agreed with Hou et al [139], who relied on ant colony optimization in agricultural machinery rental optimization. According to the data depicted in Table 6, the use of the ant colony (ABCO) algorithm was correlated with a higher quality of service value.…”

“…In a natural environment, the ant colony has an army of workers that randomly scouts for food and leave a pheromone trail to be followed. In cases when there are two trails to the food source, the ants will naturally follow the shortest path to reduce the time taken to find food [139][140][141]. The ability to find the shortest path translates to time and resource maximization in farms and better production efficiency.…”

“…The accurate estimation of the commodity prices would help guarantee profits and reduce postharvest losses, leading to better productivity on farms. However, there are inherent challenges associated with the system that guided the development of parameter adaptive artificial bee colony [170,173] and dynamic artificial bee-ant colony algorithm [139]. The iterations made in the artificial bee colony had practical benefits in agriculture; this was evident from the optimized performance of the algorithms in machinery rental optimization by Hou et al [139], and optimization of agricultural efficiency [18,170] and the management of swarm robotic bees [86,174,175].…”

“…However, there are inherent challenges associated with the system that guided the development of parameter adaptive artificial bee colony [170,173] and dynamic artificial bee-ant colony algorithm [139]. The iterations made in the artificial bee colony had practical benefits in agriculture; this was evident from the optimized performance of the algorithms in machinery rental optimization by Hou et al [139], and optimization of agricultural efficiency [18,170] and the management of swarm robotic bees [86,174,175]. Both Tang et al [20] and Nayyar et al [42] affirmed that the ABC algorithm was best suited for mapping the robot path.…”