Search Results (81)

Blast-induced flyrock is one of the most critical hazards in surface mining and quarrying, posing significant risks to occupational safety, nearby structures, and the environment. Accurate prediction of flyrock distance is therefore essential for safe blast design and effective risk management. In this study, Random Forest (RF), Extra Trees (ET), and Support Vector Regression (SVR) models were developed to predict flyrock distance, and their hyperparameters were optimized using the Secretary Bird Optimization Algorithm (SBOA) and the Spider-Tailed Horned Viper Optimizer (STHVO). Prior to optimization, six cross-validation strategies were compared using GridSearchCV to identify the most appropriate strategy for each model. The final models were evaluated on an independent test dataset using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and Nash–Sutcliffe efficiency (NSE). Model interpretability was investigated using SHapley Additive exPlanations (SHAP). The results showed that appropriate cross-validation and metaheuristic hyperparameter optimization improved predictive performance, with the ET–STHVO model achieving the best overall results. SHAP analysis identified B/D, PF, H/B, RBS, and U/B as the most influential predictors. The proposed framework provides an accurate, robust, and interpretable decision-support tool for safer blasting operations. Full article

Bio-inspiration has mainly been used to represent organisms, behaviors, or natural processes in the design of metaheuristics. This study proposes a different use: drawing on a biological phenomenon to model the temporal evolution of an internal parameter in an existing algorithm. Logarithmic Mean Optimization (LMO) is adopted as a case study, focusing on $\beta$, which scales the stochastic perturbation term and regulates the balance between exploration and exploitation. Inspired by the progressive transition observed during bird landing, a normalized arctangent trajectory controlled by m and k is proposed. Both hyperparameters were tuned through Bayesian optimization using the Tree-structured Parzen Estimator (TPE) implemented in Optuna. The 23 benchmark functions were divided into 12 tuning functions and 11 independent test functions. Nine $\beta$ configurations were evaluated through 31 runs per function. The Friedman test showed significant differences among the variants ($p=3.8269\times {10}^{-10}$), and the proposed formulation achieved the best average rank (1.7273). Post-hoc Wilcoxon tests with Holm correction found significant differences against two of the eight alternatives. Overall, the results suggest that bio-inspiration, when used as a criterion for designing adaptive parameter-control mechanisms, can yield improvements and be considered a potential alternative in the design of new metaheuristic algorithms. Full article

Visual art and graphic design tasks, such as generating a harmonious color palette or arranging the elements of a page, can be naturally formulated as mathematical optimization problems whose objective functions are non-convex, multimodal, and non-differentiable. Metaheuristic algorithms are well-suited to such problems. The secretary bird optimization algorithm (SBOA) is a recently proposed bio-inspired metaheuristic that mimics the hunting and predator-escaping behaviors of secretary birds, and it has shown competitive performance. However, SBOA still suffers from insufficient population diversity, premature convergence, and an unbalanced transition between exploration and exploitation, which limits its accuracy on complex design problems. To overcome these limitations, this paper proposes a multi-strategy secretary bird optimization algorithm (MSSBOA) that integrates three improvement strategies. First, a good point set initialization is employed to generate a low-discrepancy initial population that covers the search space more uniformly and enriches population diversity. Second, a lens opposition-based learning strategy is applied to the inferior individuals to help the population escape local optima while preserving the elite. Third, an adaptive Cauchy–Gaussian mutation is imposed on the best individual to balance global exploration and local exploitation throughout the search. The performance of MSSBOA is comprehensively examined on the CEC2017 benchmark suite in 10, 30, 50, and 100 dimensions and compared with the basic SBOA and nine state-of-the-art algorithms; the results are analyzed by the Friedman test, the Nemenyi post hoc test and the Wilcoxon rank-sum test. MSSBOA is then applied to two representative visual-design optimization problems: aesthetic color-harmony palette generation and graphic-layout aesthetics optimization. In all cases, MSSBOA outperforms the basic SBOA and the competing algorithms in terms of convergence speed, stability, and solution quality, confirming its effectiveness for computational-aesthetics applications in art design. Full article

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and avoiding local optima. To deal with this issue, this paper proposes a new method called the Blackcap Optimization Algorithm (BCOA), which is inspired by the navigation and migration behavior of Blackcap birds. Instead of using complicated distance calculations, the proposed method is based on angular movement vectors. The movement of each search agent is controlled by an angle-based mathematical model that combines the global best angle, a successful neighboring angle, and an adaptive exponential disturbance factor. In addition, the algorithm uses a quasi-genetic path transition mechanism to combine successful parent paths together, along with a territorial competition stage. This structure helps reduce computational cost and improves the balance between exploration and exploitation. The performance of the proposed algorithm is tested on 32 benchmark functions and seven engineering and network optimization problems. The simulation results show that BCOA has a good ability to avoid local optima and can achieve acceptable convergence speed and cost reduction compared to several existing methods. Full article

To address the imbalance between global exploration and local exploitation in the secretary bird optimization algorithm (SBOA), this paper presents a multi-strategy improved version termed MSISBOA. The proposed approach incorporates optimal Latin hypercube sampling during initialization to achieve a more uniform distribution of initial solutions. In the hunting phase, an adaptive Cauchy mutation factor and a boundary strategy are integrated to refine local search precision. To reduce the risk of stagnation in local optima during later iterations, a triangular walk strategy is utilized for mutation perturbation. Furthermore, the escape phase employs a combined Tent chaotic-Gaussian mutation factor and an elite retention strategy to maintain high-quality solutions while diversifying the population. The performance of MSISBOA was evaluated using the benchmark suites released for the IEEE Congress on Evolutionary Computation (CEC), including CEC-2017 and CEC-2022, against nine other swarm intelligence algorithms, with statistical results showing that MSISBOA achieved the highest average rank. Additionally, the algorithm was applied to 18 engineering optimization problems to assess its capability in solving practical constrained tasks. Experimental results indicate that MSISBOA provides competitive convergence characteristics and solution quality across the tested scenarios. Full article

To address the limitations of the original Secretary Bird Optimization Algorithm (SBOA), such as insufficient population diversity, weak local exploitation ability, and a tendency to become trapped in local optima when solving complex optimization problems, this paper proposes a Multi-Strategy Improved Secretary Bird Optimization Algorithm (MISBOA). First, a chaotic elite initialization strategy is introduced to improve the quality and diversity of the initial population. Second, an adaptive spiral Lévy flight strategy is designed to enhance the balance between global exploration and local exploitation during the iterative process. Third, a dynamic neighborhood-guided mutation strategy is incorporated to maintain population diversity and improve convergence accuracy in the later search stage. To validate the effectiveness of the proposed algorithm, MISBOA is comprehensively evaluated on the IEEE CEC2014, CEC2017, and CEC2020 benchmark suites. Experimental results demonstrate that MISBOA achieves superior convergence speed, optimization accuracy, and robustness compared with several representative metaheuristic algorithms. Furthermore, MISBOA is applied to Otsu-based multilevel threshold image segmentation. The segmentation performance is assessed using PSNR, FSIM, SSIM, and visual quality comparisons. The results indicate that MISBOA can generate more accurate and stable segmentation outcomes, demonstrating its strong potential for solving complex numerical optimization and artistic image segmentation problems. Full article

The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. Full article

Unmanned aerial vehicles (UAVs) provide a highly flexible platform for synthetic aperture radar (SAR), enabling efficient, high-quality imaging in remote sensing applications. In realistic imaging missions, regions of interest (ROIs) usually have different sizes and spatial distributions. While deploying SAR-UAVs with heterogeneous flight and imaging capabilities can improve mission time efficiency, realizing this improvement depends critically on task assignment and path planning. In this paper, the joint task assignment and path planning problem for heterogeneous SAR-UAVs in multi-region imaging missions is addressed. First, flight and imaging models of SAR-UAVs are established, and a constrained optimization problem is formulated to minimize the mission completion time. Then, an improved clustering strategy based on area-density and cost prediction (ADCP) is proposed to align ROI-dependent imaging workloads with heterogeneous SAR-UAV capabilities, thereby leveraging capability advantages and reducing the mission completion time. Finally, a discrete secretary bird optimization algorithm (DSBOA) is developed to generate feasible, high-quality paths. To accelerate convergence, UAV paths are encoded as waypoint sequences, and a mutation-based operator is introduced to update the population. Extensive Monte Carlo simulations show that the proposed approach consistently outperforms the baselines in mission completion time, demonstrating its effectiveness in improving time efficiency for multi-region SAR imaging missions. Ablation experiments further confirm the independent contributions of the proposed ADCP method and DSBOA algorithm. Full article

This paper proposes a multi-strategy Secretary Bird Optimization Algorithm (MS-SBOA) for solving global optimization problems and 3D wireless sensor network deployment. While preserving the original two-phase search framework of SBOA, the proposed algorithm achieves a dynamic balance between global exploration and local exploitation through the synergistic integration of multiple enhancement strategies, including a hybrid initialization scheme combining Latin hypercube sampling and quasi-opposition-based learning, a success-history-based adaptive parameter learning mechanism, a finance-inspired market-state trading operator, and an elite-guided population regulation strategy. Experimental results on the IEEE CEC2020 and CEC2022 benchmark test suites demonstrate that MS-SBOA significantly outperforms nine comparative algorithms, including VPPSO, IAGWO, and QHSBOA, under both 10-dimensional and 20-dimensional settings. The proposed algorithm exhibits superior optimization accuracy, faster convergence speed, and stronger robustness. Statistical analyses using the Wilcoxon rank-sum test and the Friedman mean rank test further confirm that the observed performance improvements are statistically significant. Moreover, MS-SBOA is applied to three-dimensional wireless sensor network (3D WSN) deployment optimization problems, where the average coverage rates reach 76.22% and 82.32% for 30-node and 50-node deployment scenarios, respectively. The resulting node distributions are more uniform, and the computational efficiency is improved compared with competing algorithms. Full article

This paper proposes a unified approach based on maximal consistent blocks (MCBs) to address the problem of incomplete single-label and multi-label dimensional reduction. The matrix computation method for maximal consistent blocks is improved by introducing a dynamic multi-row detection mechanism and optimizing the block size determination criteria. The complete set of maximal consistent blocks can be efficiently obtained via matrix intersection operations. For incomplete single-label decision information systems, an attribute reduction algorithm is designed based on maximal consistent blocks. Redundant attributes are eliminated by preserving the upper and lower approximation distributions of decision classes. In the multi-label scenario, a complementary decision reduct method integrating coarse and fine decision functions is proposed, and a unified solution paradigm is adopted to accomplish multi-label dimensional reduction. The effectiveness in classification (F1-score, Ranking Loss, Hamming Loss), reduction performance, and runtime efficiency is validated via statistical tests, scalability studies, structured missingness studies, and comparisons with four representative baselines on Birds, Scene, and Yeast datasets (5%/10%/15% missing rates). Full article

Cluster Head (CH) selection is a crucial process in clustered Wireless Sensor Networks (WSNs) because it directly affects energy balance and network lifetime. However, CH selection is an NP-hard optimization problem, and many metaheuristic-based methods suffer from limited search diversity and premature convergence, leading to uneven energy dissipation. This paper formulates CH selection as a multi-criteria energy-aware optimization problem and proposes an Enhanced Secretary Bird Optimization Algorithm (ESBOA). The proposed ESBOA improves the original Secretary Bird Optimization Algorithm by integrating logistic chaotic map-based population initialization to enhance early-stage exploration and an iterative local search mechanism to strengthen solution refinement in later iterations. A multi-criteria fitness function considering residual energy, distance to the base station, and node degree explicitly guides the optimization toward energy-efficient clustering. The proposed method is implemented in a Python 3.11.9-based simulation framework using a first-order radio energy model and evaluated against standard SBOA, Crested Porcupine Optimization (CPO), and Dung Beetle Optimization (DBO). Simulation results demonstrate that ESBOA preserves more alive nodes, maintains higher residual energy, delivers more cumulative packets to the base station, and extends network lifetime, achieving approximately 3–13% improvement in last node death (LND) compared with the standard SBOA. Full article

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are assessed within a dynamic three-stage flexible flow shop model under no-buffer blocking constraints. Findings from 300 Monte Carlo replications demonstrate that while the Genetic Algorithm achieves peak global efficiency, discretized bio-inspired algorithms reach a comparable statistical efficiency frontier. Notably, the discretized Secretary Bird Optimization Algorithm facilitates superior emergency integration by maintaining natural capacity buffers, whereas the aggressive local optimization characteristic of alternative methods often triggers resource saturation in recovery units. These results indicate a potential recovery of 90 annual operating hours per theater.These results indicate a potential recovery of 90 annual operating hours per theater, representing a 6.7% increase in resource utilization efficiency. This improvement provides a critical data-driven capacity margin to mitigate the non-prioritized (Non-GES) surgical backlog in Chilean public hospitals. Full article

The poultry sector plays a crucial role in global food production by meeting the growing demand for affordable, nutritious protein sources. However, it faces significant challenges in providing sustainable and cost-effective nutritional solutions that improve poultry health, performance, and product quality. Recent advancements in artificial intelligence (AI) have the potential to enhance poultry nutrition through the development of precise feeding strategies. AI helps monitor and optimize nutrient intake, thereby boosting feed efficiency, reducing waste, and lowering costs. This article examines how AI-driven innovations may advance the management of poultry feed ingredients, nutrient monitoring, and dietary formulations. By utilizing AI tools such as machine learning algorithms and real-time data analytics, poultry producers can track and assess the nutritional needs of individual birds. This allows for the development of more precise feed formulations tailored to the specific needs of different age groups, breeds, and environmental conditions. These AI technologies help select the best feed ingredients and enable precise adjustments to nutrient composition. This results in healthier birds, better feed conversion rates, and higher-quality poultry products. Additionally, AI advancements help reduce the environmental impact of poultry farming by reducing feed waste and resource consumption. This article highlights how AI-driven insights enhance decision-making, enabling the poultry industry to grow sustainably while promoting animal welfare, increasing efficiency, and producing high-quality poultry products that meet consumer expectations for both sustainability and nutritional value. Full article

In this study, an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA), named MSVSBOA, is proposed to address the limitations of the SBOA in global optimization and UAV 3D path-planning. The proposed MSVSBOA integrates three complementary strategies to achieve a balanced exploration and exploitation trade-off. First, a Levy-based Directed Exploration mechanism is introduced to enrich the global search capability and prevent premature convergence. Second, a spiral movement mechanism is incorporated to strengthen the local exploitation behavior and improve convergence accuracy. Third, a Differential Evolution-inspired refinement strategy (DE-Refinement) is employed to accelerate fine-grained exploitation during the later stages of optimization. The performance of the MSVSBOA is extensively evaluated on the CEC 2014 and CEC 2022 benchmark suites. Experimental results demonstrate that the MSVSBOA achieves superior accuracy, faster convergence, and improved robustness compared to the SBOA and other multi-strategy variants. Furthermore, the MSVSBOA is applied to a challenging UAV 3D path planning problem, where it successfully generates safe, smooth, and collision-free trajectories while outperforming competing algorithms. These findings confirm the effectiveness of the proposed MSVSBOA for both global optimization problems and real-world UAV applications. Full article

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article