Analysing Convergence, Consistency, and Trajectory of Artificial Bee Colony Algorithm

Bansal, Jagdish Chand; Gopal, Anshul; Nagar, Atulya K.

doi:10.1109/access.2018.2884255

Cited by 17 publications

(4 citation statements)

References 28 publications

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“…Therefore, the convergence speeds and effects of the DABAA and MMAS are the same. The ABC algorithm is convergent, while Equation ( 11) is satisfied [32]. Therefore, the DABAA is verified to be convergent according to Equation (11) [33].…”

Section: Convergence Analysis Of the Dabaamentioning

confidence: 94%

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

Hou

Zhang

et al. 2022

Algorithms

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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.

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Section: Convergence Analysis Of the Dabaamentioning

confidence: 94%

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

Hou

Zhang

et al. 2022

Algorithms

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“…MapReduce-based optimization methods improve search quality and local search time, combining features of both MapReduce and specific methods [29]. Parallel ABC models in MapReduce leverage the parallelization capabilities of MapReduce and can handle large populations [30]. The application of the Modified Artificial Bee Colony Algorithm (MABC) optimizes cloud resource management, enhancing resource utilization significantly [31].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Big Data Clustering: The Improved K-Means - Artificial Bee Colony Algorithm with MapReduce

Banait,

Kaklij,

Bankar

et al. 2023

Preprint

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The volume and diversity of data produced by scientific applications and the corporate world have increased drastically in the modern era. Large data is challenging to collect, store, transform, and analyze. Processing vast amounts of data is a challenging task, and one major problem with big data is that it takes longer to run typical algorithms. One of the most common data mining jobs is clustering. It is applied in several fields. The K-Means clustering algorithm is widely recognized as one of the prominent unsupervised learning techniques in machine learning. The advantages involve fundamental clarity, good effect, and ease of execution. As the online world grew quickly, the rise in data collection locations occurred simultaneously, marking the era of big data and an explosion of information. This research introduces the IK-ABC Algorithm (Improved K Means - Artificial Bee Colony) to tackle various challenges encountered in k-means clustering algorithms. These issues encompass limitations in global search capabilities, the sensitivity of cluster center selection, randomness in initialization, early-stage development, and sluggish convergence observed in the original artificial bee colony algorithm. To expedite computation and enhance the effectiveness of the iterative optimization process, a custom fitness function tailored for the K-means clustering technique and a position update formula relying on global guidance was created through the utilization of MapReduce.

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“…According to [25], MMAS is convergent. So, DAABA is verified to be convergent according to [26] and the following equation:…”

Section: Convergence Analysis Of Daabamentioning

confidence: 99%

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

Zhou

Wen

et al. 2020

Mathematical Problems in Engineering

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In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms.

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Analysing Convergence, Consistency, and Trajectory of Artificial Bee Colony Algorithm

Cited by 17 publications

References 28 publications

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

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

Enhancing Big Data Clustering: The Improved K-Means - Artificial Bee Colony Algorithm with MapReduce

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

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