Cloud Service Optimization Method Based on Dynamic Artificial Ant-Bee Colony Algorithm in Agricultural Equipment Manufacturing

Zhou, Kaixiong; Wen, Yongzhao; Wu, Wanying; Ni, Zhiyong; Jin, Tianguo; Long, Xiaojun

doi:10.1155/2020/9134695

Cited by 12 publications

(10 citation statements)

References 24 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature [ 18 ] extracted the tendentiousness and emotion information contained in film reviews. Experimental outcome shows that this means is available in mining and summarizing movie reviews.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Liu

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reason, this paper proposes an improved neural network optimized by mutation ant colony algorithm for automatic acquisition of film labels, which not only overcomes the disadvantages of traditional neural network, such as difficulty in determining weights, slow convergence speed, and easiness to fall into local minimum, but also makes up for the shortcomings faced by using ant colony algorithm alone through the gradient information of quantum genetic algorithm neural network. The results show that the user similarity judgment is added in the process of calculating the user rating deviation between movies, and the neighbor chooses to add the movie tag weight and rating similarity as the basis for the neighbor selection of the target movie in the process of predicting the target movie rating. Experiments show the effectiveness of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

Liu

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The location of the nectar sources corresponds to the solution of the problem, and the amount of nectar in the nectar sources is related to the fitness of the solution. [36][37][38] In this article, the dimension of the nectar source is set to D, as shown in Equation (2). All nectar sources in the population have the same dimension, and the number of dimensions updated in each run is represented by m. The traditional ABC performs neighborhood search by changing one dimension at a time, that is, m = 1.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A self‐learning artificial bee colony algorithm based on reinforcement learning for a flexible job‐shop scheduling problem

Long

Zhang

et al. 2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

The flexible job-shop scheduling problem (FJSP) is currently one of the most critical issues in process planning and manufacturing. The FJSP is studied with the goal of achieving the shortest makespan. Recently, some intelligent optimization algorithms have been applied to solve FJSP, but the key parameters of intelligent optimization algorithms cannot be dynamically adjusted during the solution process. Thus, the solutions cannot best meet the needs of production. To solve the problems of slow convergence speed and reaching a local optimum with the artificial bee colony (ABC) algorithm, an improved self-learning artificial bee colony algorithm (SLABC) based on reinforcement learning (RL) is proposed. In the SLABC algorithm, the number of updated dimensions of the ABC algorithm can be intelligently selected according to the RL algorithm, which improves the convergence speed and accuracy. In addition, a self-learning model of the SLABC algorithm is constructed and analyzed using Q-learning as the learning method of the algorithm, and the state determination and reward methods of the RL algorithm are designed and included in the environment of the artificial bee colony algorithm.Finally, this article verifies that SLABC has excellent convergence speed and accuracy in solving FJSP through Brandimarte instances. K E Y W O R D Sartificial bee colony, flexible job-shop scheduling problem, reinforcement learning, self-learning artificial bee colony INTRODUCTIONThe flexible job-shop scheduling problem (FJSP) is an extension of the classic job-shop scheduling problem, and it is a complex combinatorial optimization problem. 1 The FJSP has been a research hotspot over the years. In recent years, artificial intelligence optimization algorithms such as the ant colony optimization algorithm (ACO), 2,3 genetic algorithm (GA), 4-6 bee colony algorithm, [7][8][9][10][11] and various hybrid algorithms [12][13][14][15] have been usedto solve this problem, and some progress has been achieved, but a set of completely good solutions has not yet been reached; therefore, there is room for further research on this problem.Job-shop scheduling is a processing resource allocation problem. It reasonably arranges production resources, processing time, processing sequence, and so on, according to existing constraints to obtain the optimal cost or efficiency. 16 Due to the NP-hard characteristics of the FJSP, it is difficult to achieve global optimization even for small problems. Therefore, many researchers have begun to develop more effective solutions to obtain near-optimal solutions. Because of this trend, to solve the combinatorial optimization problem, many optimization algorithms have been developed. Wang et al. 17 proposed a random weighted hybrid particle swarm optimization algorithm (PSO) based on the second-order oscillation,

show abstract

“…Cao et al proposed a depth optimization analysis method based on the GA algorithm [16]. 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]. All the above studies on solving problems with artificial intelligence algorithms have achieved good results, but there are few applications in agriculture.…”

Section: Introductionmentioning

confidence: 99%

“…According to data analysis, there is a certain functional relationship between the dynamic coefficient and the urgency of time; that is, the higher the UT of a task is, the greater the proportion of the UT of the task in the QoS evaluation model. According to the research of Zhou et al [17] and the verification of this paper, the calculation method of the dynamic coefficient P is shown in Equation (1):…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Hou

Zhang

et al. 2022

Algorithms

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Cloud Service Optimization Method Based on Dynamic Artificial Ant-Bee Colony Algorithm in Agricultural Equipment Manufacturing

Cited by 12 publications

References 24 publications

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

[Retracted] Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm

A self‐learning artificial bee colony algorithm based on reinforcement learning for a flexible job‐shop scheduling problem

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

Contact Info

Product

Resources

About