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Petrarch's fragmenta : the narrative and theological unity of Rerum vulgarium fragmenta
\"Building on recent Petrarch scholarship and broader studies of medieval poetics, poetic narrativity and biblical intertextuality, this study argues that Petrarch's Rerum vulgarium fragmenta is an ordered and coherent work unified by narrative and theological structures. The author begins with the premise that the multiple voices of the Petrarchan figure (or subject) call for a reading informed by historical and autobiographical considerations. Within such a reading, the internal chronology of the work coincides with a temporal framework provided by Petrarch's Latin prose and poetry. Drawing on this material, he argues that Petrarch's derivations from early poets in the Italian vernacular, his Augustineanism and his humanism are manifest in the Fragmenta and contribute to its narrative and theological unity.\"-- Provided by publisher.
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A study on rolling bearing fault diagnosis using RIME-VMD
To address the challenges of feature extraction in Variational Mode Decomposition (VMD) for rolling bearing fault diagnosis, this paper proposes a feature extraction method optimized by the RIME algorithm, called RIME-VMD. First, under various rolling bearing fault conditions, the RIME algorithm is employed to determine the optimal combination of decomposition components and penalty factors in VMD. Next, the kurtosis values of each decomposed Intrinsic Mode Function (IMF) are calculated, and the component with the most prominent fault features is selected for noise reduction through reconstruction. Finally, the sample entropy of the reconstructed signal is calculated as a fault feature and input into a Support Vector Machine (SVM) for rapid identification and diagnosis of various rolling bearing fault types. Simulation results indicate that, compared to the Whale Optimization Algorithm optimized VMD (WOA-VMD), the RIME algorithm optimized VMD (RIME-VMD) achieves shorter search times and higher search efficiency. It facilitates faster identification of decomposition parameters under various fault conditions, enhancing the robustness of fault signal detection and enabling rapid, efficient identification of rolling bearing faults. The findings of this study offer guidance and reference for future research on rolling bearing fault diagnosis.
Journal Article
SRIME: a strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems
This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary algorithm (EA) inspired by the soft and hard rime growth process of rime-ice, which has a powerful exploitation ability. But in complex optimization problems, RIME will easily trap into local optima and the optimization will become stagnation. Noticing this issue, we introduce three techniques to the original RIME: (1) Latin hypercube sampling replaces the random generator as the initialization strategy, (2) modified hard rime search strategy, and (3) embedded distance-based selection mechanism. We evaluate our proposed SRIME in 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions and eight real-world engineering optimization problems with nine state-of-the-art EAs. Experimental and statistical results show that the introduction of three techniques can significantly accelerate the optimization of the RIME algorithm, and SRIME is a competitive optimization technique in real-world applications. Ablation experiments are also provided to analyze our proposed three techniques independently, and the embedded distance-based selection contributes most to the improvement of SRIME. The source code of SRIME can be found in
https://github.com/RuiZhong961230/SRIME
.
Journal Article
An algorithm for lane detection based on RIME optimization and optimal threshold
In order to address the challenges of low lane line detection rates caused by complex road conditions,we propose a novel algorithm that integrates frost and ice optimisation with optimal thresholding. A pre-processing model based on Retinex theory is used to reduce noise and preserve grey scale detail. The optimal OTSU threshold is determined for segmentation, which is enhanced by tent mapping. To further enhance the precision of the detection process, the binarized image is transformed into a bird’s-eye view, and the lane line pixel features are identified through the use of an adaptive sliding window. Ultimately, the RANSAC algorithm is utilized in conjunction with a parabolic model for lane line fitting. The experimental results demonstrate that, in comparison to similar image segmentation algorithms, the proposed method exhibits a notable advantage in terms of threshold calculation error and computational efficiency. Moreover, in comparison to analogous line detection algorithms, the detection accuracy rate reaches 93.87%, effectively reducing the impact of interference factors and demonstrating remarkable robustness that surpasses the traditional Hough Transform, which has an accuracy of 43.2%, and sliding window and Hough transform, with an accuracy of 89.16%. The code of our research work is publicly available at:
https://github.com/zx2000430/rime
.
Journal Article
A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization
The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.
Journal Article
An improved RIME optimization algorithm based maximum power point tracking method for photovoltaic system under partially shading condition
Maximum power point tracking (MPPT) is a pivotal technology for photovoltaic (PV) systems. Due to variations in light intensity and temperature, the output characteristic curve of the photovoltaic system exhibits multi-peak phenomena, and traditional MPPT algorithms perform poorly in complex and changing environments. Therefore, this paper proposes an MPPT method grounded on an improved RIME optimization algorithm (IRIME). This approach enhances the algorithm’s exploratory capabilities by incorporating logistic mapping during the initialization stage. Furthermore, it optimizes the algorithm’s parameters through sequences generated by piecewise mapping, thereby realizing a harmonious balance between global exploration and local exploitation. Additionally, the introduction of an adaptive inertia weight dynamically adjusts the search strategy, thereby increasing the adaptability, convergence speed, and search efficiency of algorithm. Compared to PSO-MPPT and RIME-MPPT, the proposed method reduced the average tracking time by 0.085 s and 0.425 s, respectively. Additionally, in terms of maximum output power, the proposed method achieved an average improvement of 0.97% and 3.48% over the aforementioned methods, respectively. Particularly in PV system simulations under varying irradiance and temperature conditions, the proposed method consistently achieves the best results, verifying its efficient, stable, and fast-converging characteristics in MPPT strategies for PV systems.
Journal Article
Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models
As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.
Journal Article
Improved Snake Optimization Algorithm for Global Optimization and Engineering Applications
In engineering applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The Snake Optimization Algorithm (SO) is a novel metaheuristic method with widespread use. However, SO has limitations, including reduced search efficiency in later stages and a tendency to get trapped in local optima, preventing full exploration of the solution space. To overcome these, this paper introduces the Multi-strategy Improved Snake Optimization Algorithm (ISO), which integrates six key strategies. First, the Sobol sequence is used for population initialization, ensuring uniform distribution and enhancing global exploration. Second, the RIME algorithm accelerates convergence and improves exploitation. Lens reverse learning further promotes exploration, avoiding local optima. Levy flight facilitates large random steps, balancing exploration and refinement. Adaptive step-size adjustment dynamically tunes the step size based on fitness, optimizing exploration-exploitation. Lastly, the Brownian random walk introduces local perturbations to fine-tune solutions. These strategies collectively improve convergence speed, stability, and optimization capability, ensuring an effective balance between exploration and exploitation. The ISO population distribution was evaluated using three uniformity algorithms: Average Nearest Neighbor Distance, Star Discrepancy, and Sum of Squared Deviations (SSD). ISO demonstrated improvements of 63.08%, 26.09%, and 8.88%, respectively, over SO. Its exploration-exploitation balance and convergence were analyzed on the 30-dimensional CEC-2017 benchmark functions. Additionally, ISO was tested on 23 classic benchmark functions, CEC-2011, and CEC-2017 benchmark functions. Results showed ISO’s superior performance in convergence speed, stability, and global optimization. Furthermore, ISO was successfully applied in four engineering domains: UAV path planning, robot path planning, wireless sensor network node deployment, and pressure vessel design. In all cases, ISO outperformed SO with rapid convergence and strong robustness, achieving performance improvements of 5.69%, 34.61%, 20.73%, and 7.8%, respectively, underscoring its superior efficacy in practical applications.
Journal Article