Articles
Publication Date: 2026
International Journal of Engineering, Transactions A: Basics (17281431)39(10)pp. 2529-2545
This study proposes the Bermuda Weed Optimization (BWO) algorithm; a novel and scalable metaheuristic algorithm inspired by the invasive growth of Bermuda grass. This algorithm has been developed as an enhanced version of the Invasive Weed Optimization (IWO) algorithm and, by imitating the plant's robust propagation strategies, achieves a better balance between global exploration and local exploitation. The algorithm's performance was rigorously evaluated against four previous IWO-based versions, and its superior scalability was demonstrated through the lowest average error and stable performance across diverse scenarios. Furthermore, BWO was compared with the new Gray Squirrel Search Algorithm (GSFA)—which falls outside the IWO category—to assess its performance against a novel method unrelated to the IWO family; this comparison highlighted BWO's competitive superiority and achieved an average 64.43% improvement in best-cost results. The strong convergence and scalability of BWO make it highly suitable for real-time applications—particularly in automotive systems. In a practical implementation using a cloud-based cruise control (CC) framework, BWO significantly outperformed the RPO-based method (the latest approach) by reducing overshoot by 45.92%, settling time by 29.38%, ISE speed by 8.92%, and maximum jerk by 20.09%. By achieving near-optimal convergence and leveraging cloud deployment with high scalability, BWO can effectively adapt to diverse automotive system requirements and achieve high efficiency across multiple operating modes. © 2026 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
Publication Date: 2025
Digital Communications and Networks (23528648)11(2)pp. 574-586
In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding (RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC (ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks. © 2024 Chongqing University of Posts and Telecommunications
