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The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter and bandwidth of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes.

Optimization is the key to obtaining efficient utilization of resources in structural design. Due to the complex nature of truss systems, this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints. Two new algorithms, the Red Kite Optimization Algorithm (ROA) and Secretary Bird Optimization Algorithm (SBOA), are utilized on five benchmark trusses with 10, 18, 37, 72, and 200-bar trusses. Both algorithms are evaluated against benchmarks in the literature. The results indicate that SBOA always reaches a lighter optimal. Designs with reducing structural weight ranging from 0.02% to 0.15% compared to ROA, and up to 6%–8% as compared to conventional algorithms. In addition, SBOA can achieve 15%–20% faster convergence speed and 10%–18% reduction in computational time with a smaller standard deviation over independent runs, which demonstrates its robustness and reliability. It is indicated that the adaptive exploration mechanism of SBOA, especially its Levy flight–based search strategy, can obviously improve optimization performance for low- and high-dimensional trusses. The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA, a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.

This paper proposes a UAV path planning method based on a Multi-strategy Secretary Bird Optimization Algorithm (MSBOA) to address the challenges of navigating complex terrain. First, a pooling mechanism is introduced to enhance population diversity and improve the algorithm’s optimization capabilities, balancing global exploration and local exploitation. Second, a dynamic fitness distance balance technique is incorporated to balance exploration and exploitation, preventing the population from becoming trapped in local optima while improving convergence accuracy. Finally, a greedy selection-based centroid reverse learning approach is used to update the population, enhancing the algorithm’s exploratory performance. To validate the effectiveness of the proposed improved algorithm, the proposed MSBOA is compared with classical and advanced intelligent algorithms by solving the CEC2017 benchmark test functions and a designed UAV environment model. Comparative analysis of simulation results indicates that the proposed MSBOA converges faster and achieves higher accuracy than the traditional SBOA. It effectively handles complex UAV path planning problems, enabling the design of faster, shorter and safer flight paths. This further demonstrates the excellent performance of the multi-strategy SBOA in UAV path planning, highlighting its broad application prospects.

To address the issues of low convergence accuracy and susceptibility to local optima in the Secretary Bird Optimization Algorithm (SBOA), this paper proposes an improved algorithm (UTFSBOA) that integrates multi-strategy collaboration and Cauchy-Gaussian crossover. The algorithm introduces three innovative mechanisms. First, it incorporates an adaptive nonlinear factor-based directional search mechanism to enhance global exploration. Second, it uses an exponentially decaying energy escape factor inspired by Harris Hawk Optimization (HHO) to balance exploration and exploitation. Third, it includes a Cauchy-Gaussian crossover strategy to enrich solution diversity and prevent premature convergence. Experimental evaluations on the CEC2005 benchmark functions demonstrate that UTFSBOA achieves 81.18% and 88.22% improvements in average convergence accuracy over SBOA in 30-dimensional and 100-dimensional scenarios, respectively. Among 12 complex functions in the CEC2022 test set, the proposed algorithm obtains optimal solutions for 7 functions. Statistical validation via Wilcoxon rank-sum and Friedman tests confirms its robustness. Validation through four real-world engineering problems further confirms its superiority in constrained and discrete optimization scenarios, with objective function improvements reaching up to 91.3%. The results prove that multi-strategy synergy significantly enhances algorithmic robustness in high-dimensional complex spaces, establishing UTFSBOA as an effective solution for constrained and discrete optimization challenges.

An improved Secretary Bird Optimization Algorithm (ISSBOA) is proposed. First, an independent thinking mechanism (IM) enhances the algorithm’s ability to avoid local optima traps and broadens global exploration during the optimization process. Second, a sine-square step size mechanism (SM) dynamically adjusts the search step size, effectively balancing the performance deficiencies of the Secretary Bird Optimization Algorithm (SBOA) in both the exploration and exploitation phases. To validate the effectiveness of ISSBOA, simulations are conducted on the IEEE CEC2017 benchmark test suite, with comparisons made against 7 classic metaheuristic algorithms and seven recently proposed improved algorithms. The results demonstrate that ISSBOA achieves optimal performance in two sets of comparison experiments: when compared with the 7 standard algorithms, ISSBOA outperforms them in terms of average fitness value on 23 out of 30 test functions and in terms of variance on 16 functions, achieving an average Friedman test rank of 1.80 (securing first place); when compared with the 7 high-efficiency improved algorithms, it excels in average fitness value on 19 functions and in variance on 15 functions, with an average Friedman test rank of 2.73 (ranking first). This indicates that the proposed ISSBOA has both high optimization accuracy and strong robustness. Additionally, an adaptive transformation function is used to convert the continuous-domain ISSBOA into a binary version (BISSBOA) for discrete optimization tasks such as feature selection. To validate the performance of BISSBOA, a comprehensive evaluation is conducted using 20 public datasets with different dimensions, and comparisons are made against 7 high-performance feature selection algorithms. The results show that BISSBOA outperforms the other comparative algorithms across five evaluation metrics, thereby confirming its practicality and superiority in the field of feature selection.

Applications designed for real-time IoT operations improve cloud-based service utilization due to their rapid scalability. Though cloud computing appears to be more effective for data processing and storage in a range of IoT applications, its real-time scalability presents issues in fulfilling the demands of network bandwidth and latency-sensitive applications. In this context, fog computing is shown to be a complementary paradigm to cloud computing, providing extra benefits and capabilities aimed at extending cloud services to end users and edge devices. Due to the restricted capabilities of fog nodes, only lightweight activities can be conducted locally, while jobs requiring more processing time are handled in the cloud. As a result, an Improved Multi-Strategy Enhanced Secretary Bird Optimization Algorithm using Reinforcement Learning (IMSESBOA + RL) for IoT Task Scheduling (TS) mechanism is presented to reduce data processing time and enhance Quality of Service (QoS) in fog-cloud computing. This IMSESBOA + RL approach is designed as an efficient scheduling model that investigates and processes various scalable quantities of tasks while minimizing latency and energy costs. It used a multi-objective methodology based on Secretary Bird Optimization Algorithm’s (SBOA) balanced exploration and exploitation capabilities, which has multi-strategy benefits in terms of maximizing resource consumption rate and shortening makespan. It further uses RL for dynamically adapting to the new workloads by excelling in learning optimal strategies using the interaction of trial and error with the environment. The simulation findings of the IMSESBOA + RL approach verified that it reduced makespan by 19.42% and execution time by 18.32% compared to the baseline approaches with various jobs originating from IoT applications.

The increasing use of Unmanned Aerial Vehicles (UAVs) in mission-critical operations has intensified the need for efficient, scalable, and adaptive clustering in Flying Ad Hoc Networks (FANETs). This paper presents a clustering optimization framework based on the Secretary Bird Optimization Algorithm (SBOA), a bio-inspired metaheuristic that simulates the strategic hunting behavior of the secretary bird. Compared to the Fire Hawk Optimization Algorithm (FHOA), the Portia Spider Optimization Algorithm (PSOA), and multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP), SBOA results in a balanced exploration–exploitation trade-off that facilitates dynamic and energy-efficient cluster head (CH) selection in high-mobility 3D UAV networks. It is modeled as a multi-objective optimization problem with the aim of minimizing intra-cluster distance, maximizing residual energy, and load balancing. The population of UAVs ranges from 30 to 160 nodes, the communication range from 100 to 900 m, and the 3D grid scale. It emerges that SBOA outperforms all its counterparts in terms of up to 15% higher optimization fitness, 10% higher cluster density, and 40% reduced load imbalance. SBOA’s superiority with respect to convergence stability, cluster uniformity, and CH workload distribution is further validated using several visualization tools like heatmaps, t-SNE projections, and statistical plots. SBOA has also been able to achieve over 85% optimal fitness even in highly sparse environments to establish its scalability and robustness. Statistical validation confirmed that SBOA significantly outperformed FHOA, PSOA, and MOSFP to achieve as high as 0.15 higher fitness, with p  < 0.001, reduced the convergence time by almost four frames, with p  = 0.003, provided 40% lower load imbalance, with p  < 0.001, with consistently tighter cluster stability distributions to validate its robustness for large-scale real-time FANET deployments. Such findings make SBOA a viable and high-performance clustering solution for next-generation, real-time, energy-constrained FANET deployments in critical and dynamic environments. SBOA may be extended to incorporate mobility prediction and energy-aware routing to enhance real-time scalability in larger and more dynamic FANET scenarios using a hybrid approach.

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.

Automatic generation control (AGC) is vital for preserving frequency and power balance in modern interconnected power systems (IPSs), which are increasingly challenged by nonlinear dynamics, parameter uncertainties, and unpredictable load variations. This study introduces a three‐level neuro‐fuzzy‐tilt‐derivative controller with a low‐pass filter ( 3 TLNF‐TDN), designed to achieve intelligent, robust, and adaptive control performance in AGC applications. The controller parameters are optimally tuned using the secretary bird optimization algorithm (SBOA), a recently developed metaheuristic inspired by the predatory behavior of secretary birds. To validate the effectiveness of the proposed approach, a three‐area hydro‐thermal‐gas power system model incorporating realistic nonlinearities is developed. The performance of the 3 TLNF‐TDN controller is comprehensively compared with several benchmark AGC controllers, including the tilt‐derivative with filter coefficient (TDN) controller, the single‐level neuro‐fuzzy TDN ( 1 SLNF‐TDN) controller, and the two‐level neuro‐fuzzy TDN ( 2 TLNF‐TDN) controller. Simulation results demonstrate that the proposed controller exhibits markedly superior dynamic performance, yielding faster settling time (ST), reduced overshoot (OS)/undershoot (US), and lower frequency oscillations, alongside improved performance indices under varying load disturbances and parameter uncertainties. Moreover, the integration of a thyristor‐controlled series capacitor (TCSC) further enhances tie‐line power flow and frequency stability. Overall, the results confirm that the SBOA‐optimized 3 TLNF‐TDN controller provides a high degree of robustness, adaptability, and practical feasibility for real‐world AGC implementations.

Dermatological diseases are prevalent globally and provide significant challenges in diagnosis and treatment. Dermatology has changed due to developments in high-resolution digital photography and medical imaging, making it possible to document and analyze skin, nail, and hair diseases in great detail. With more than 10,000 photos, the Skin Condition Image Network (SCIN) dataset has become an essential tool in this area. In dermatological image analysis, image segmentation is essential because it makes it easier to identify and classify areas of interest for use, including automated disease diagnosis, lesion identification, and measurement. However, because skin textures vary, lighting varies, and skin disorders appear differently individually, it is difficult to achieve reliable segmentation in dermatological images. While segmentation techniques are now helpful for broad image analysis jobs, they are frequently insufficient for dermatological images from datasets such as SCIN. Reliable and consistent segmentation results are hampered by problems such as uneven lighting, different lesion scales, and image artifacts. Therefore, particular optimization algorithms that can adapt to the unique characteristics of dermatological images are needed to increase segmentation accuracy. This work is designed explicitly for SCIN dermatological images, suggesting an enhanced multilevel image segmentation optimization method. Opposition-Based Learning (OBL) and Orthogonal Learning (OL) are two improvements that the Enhanced Secretary Bird Optimization Algorithm (mSBOA) uses to increase segmentation accuracy, robustness to image artifacts, and computational efficiency. This study aims to improve optimization algorithms for robust multilevel feature segmentation in SCIN dataset dermatological images, mitigate problems such as overlapping textures and variable illumination, increase computational efficiency without sacrificing accuracy, and investigate possible clinical benefits of higher segmentation accuracy in automated dermatological diagnostics. Accurate segmentation can help create personalized treatment approaches, enhance patient outcomes, and lower diagnostic errors. Dermatologists gain from the wider adoption of AI-based healthcare solutions made possible by strong segmentation algorithms, especially in distant or underdeveloped areas. By increasing the potential for automated dermatological evaluations and enhancing diagnostic capacities, the study’s findings advance the field of dermatological image analysis.