Edge Network Data Scheduling Optimization Method Integrating Improved Jaya and Cluster Center Selection Algorithm

YANG Wensheng; PAN Chengsheng

doi:10.11999/JEIT250317

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YANG Wensheng, PAN Chengsheng. Edge Network Data Scheduling Optimization Method Integrating Improved Jaya and Cluster Center Selection Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250317

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YANG Wensheng, PAN Chengsheng. Edge Network Data Scheduling Optimization Method Integrating Improved Jaya and Cluster Center Selection Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250317

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Edge Network Data Scheduling Optimization Method Integrating Improved Jaya and Cluster Center Selection Algorithm

doi: 10.11999/JEIT250317 cstr: 32379.14.JEIT250317

YANG Wensheng,
PAN Chengsheng^,

School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (61931004)

Received Date: 2025-04-27
Rev Recd Date: 2025-07-27

Available Online: 2025-08-04

Abstract

Abstract

Objective The rapid advancement of technologies such as artificial intelligence and the Internet of Things has placed increasing strain on traditional centralized cloud computing architectures, which struggle to meet the communication and computational demands of large-scale data processing. Due to the physical separation between cloud servers and end-users, data transmission typically incurs considerable latency and energy consumption. Therefore, edge computing—by deploying computing and storage resources closer to users, has emerged as a viable paradigm for supporting data-intensive and latency-sensitive applications. However, effectively addressing the challenges of data-intensive services in edge computing environments, such as efficient edge node clustering and resource scheduling, remains a key issue. This study proposes a data scheduling optimization method for edge networks that integrates an improved Jaya algorithm with a cluster center selection strategy. Specifically, for data-intensive services, the method partitions edge nodes into clusters and identifies optimal cluster centers. Data are first aggregated at these centers before being transmitted to the cloud. By leveraging cluster-based aggregation, the method facilitates more efficient data scheduling and improved resource management in edge environments. Methods The proposed edge network data scheduling optimization method comprises two core components: a shortest-path selection algorithm based on an improved Jaya algorithm and an optimal cluster center selection algorithm. The scheduling framework accounts for both the shortest communication paths among edge nodes and the availability of network resources. The improved Jaya algorithm incorporates a cosine-based nonlinear decay function and a multi-stage search strategy to dynamically optimize inter-node paths. The nonlinear decay function modulates the variation of random factors across iterations, allowing adaptive adjustment of the algorithm’s exploration capacity. This mechanism helps prevent premature convergence and reduces the likelihood of becoming trapped in local optima during the later optimization stages. To further enhance performance, a multi-stage search strategy divides the optimization process into two phases: an exploration phase during early iterations, which prioritizes global search across the solution space, and an exploitation phase during later iterations, which refines solutions locally. This staged approach improves the trade-off between convergence speed and solution accuracy, increasing the algorithm’s robustness in complex edge network environments. Based on the optimized paths and available bandwidth, a criterion is established for selecting the initial cluster center. Subsequently, a selection scheme for additional cluster centers is formulated by evaluating inter-cluster center distances. Finally, a partitioning method assigns edge nodes to their respective clusters based on the optimized topology. Results and Discussions The simulation experiments comprise two parts: performance evaluation of the improved Jaya algorithm (Jaya*) and analysis of the cluster partitioning scheme. To assess convergence speed and optimization accuracy, three benchmark test functions are used to compare Jaya* with four existing algorithms: Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and the standard Jaya algorithm. Building on these results, two additional experiments—cluster center selection and cluster partitioning—are conducted to evaluate the feasibility and effectiveness of the proposed optimal cluster center selection algorithm for resource scheduling. A parameter sensitivity analysis using the multi-modal Rastrigin function is performed to investigate the effects of different population sizes and maximum iteration counts on optimization accuracy and stability (Table 2 and Table 3). The optimal configuration is determined to be $ {\text{po}}{{\text{p}}_{{\text{size}}}} = 50 $ and $ {t_{\max }} = 500 $, which achieves a favorable balance between accuracy and computational efficiency. Subsequently, a multi-algorithm comparison experiment is carried out under consistent conditions. The improved Jaya algorithm outperforms the four alternatives in convergence speed and optimization accuracy across three standard functions: Sphere (Fig. 4), Rastrigin (Fig. 5), and Griewank (Fig. 6). The algorithm also demonstrates superior stability. Its convergence trajectory is characterized by a rapid initial decline followed by gradual stabilization in later stages. Based on these findings, the cluster center selection algorithm is applied to tactical edge networks of varying scales—25, 38, and 50 nodes (Fig. 7). The parameter m_i is calculated (Fig. 8), and various numbers of cluster centers are set to complete center selection and cluster member assignment (Table 5). Evaluation using the Average Sum of Squared Errors (AvgSSE) under different cluster center counts reveals that the minimum AvgSSE for all three network sizes occurs when the number of cluster centers is 4 (Table 6), indicating that this configuration yields the optimal clustering outcome. Therefore, the proposed method effectively selects cluster centers and derives the optimal clustering configuration (Fig. 9), while maintaining low clustering error and enhancing the efficiency and accuracy of resource scheduling. Finally, in a 38-node edge network scenario with four cluster centers, a multi-algorithm cluster partitioning comparison is conducted (Table 7). The improved Jaya algorithm achieves the best AvgSSE result of 16.22, significantly outperforming the four baseline algorithms. These results demonstrate its superiority in convergence precision and global search capability. Conclusions To address data resource scheduling challenges in edge computing environments, this study proposes an edge network data scheduling optimization method that integrates an improved Jaya algorithm with a cluster center selection strategy. The combined approach achieves high clustering accuracy, robustness, and generalization performance. It effectively enhances path planning precision and central node selection, leading to improved data transmission performance and resource utilization in edge networks.
- Edge network,
- Data scheduling,
- Data aggregation,
- Cluster center selection,
- Path planning

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References(22)

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