Search Results (68)

The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article

As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This paper presents a dynamic-proportion Bat–Cuckoo Search (BA-CS) Hybrid Algorithm enhanced with wind field perception to tackle the challenges of UAV path planning in urban environments with strong winds, specifically addressing the issues of insufficient environmental adaptation and the exploration–exploitation imbalance. The algorithm integrates a dual-feedback mechanism that dynamically modifies the ratio of the BA/CS subpopulations in accordance with real-time iteration progress and population diversity. By incorporating wind field perception into population initialization, interpopulation information exchange, and wind resistance perturbation strategies, it attains efficient path optimization under multiple constraints. Experimental results under strong winds with speeds ranging from 10.8 to 13.8 m/s indicate that the proposed algorithm generates paths that are smooth, continuous, and entirely collision-free. It achieves a superior average wind resistance cost of 0.92, which is 9.8%, 17.1%, and 52.6% lower than those of the A*, RRT, and PSO algorithms, respectively. With a planning time of 3.95 s, it satisfies the path wind resistance stability requirements stipulated in the GB/T 38930-2020 standard, providing an effective solution for UAV inspection and emergency rescue operations in urban wind scenarios. Full article

This paper introduces an intelligent fault-diagnosis framework for power transformers that integrates hybrid machine-learning models with nature-inspired optimization. Current signals were acquired from a laboratory-scale three-phase transformer under both healthy and various fault conditions. A suite of 41 discriminative features was engineered from time–frequency and sparse representations generated via Discrete Wavelet Transform (DWT) and Matching Pursuit (MP). The resulting dataset of 2400 labeled segments was used to develop four hybrid models, PSO-SVM, PSO-RF, BA-SVM, and BA-RF, wherein Particle Swarm Optimization (PSO) and the Bees Algorithm (BA) served as wrapper optimizers for simultaneous feature selection and hyperparameter tuning. Rigorous evaluation with 5-fold and 10-fold cross-validation demonstrated the superior performance of Random Forest-based models, with the BA-RF hybrid achieving peak performance (98.33% accuracy, 99.09% precision). The results validate the proposed methodology, establishing that the fusion of wavelet- and MP-based feature extraction with metaheuristic optimization constitutes a robust and accurate paradigm for transformer fault diagnosis. Full article

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article

The main objective of this paper is to develop a maximum power point tracker (MPPT) for a photovoltaic module array (PVMA) under conditions of partial shading and sudden changes in solar irradiance. PVMAs exhibit nonlinear characteristics with respect to temperature and solar irradiance conditions. Therefore, when some modules in the array are shaded or when there is a sudden change in solar irradiance, the maximum power point (MPP) of the array will also change, and the power–voltage (P-V) characteristic curve may exhibit multiple peaks. Under such conditions, if the tracking algorithm employs a fixed step size, the time required to reach the MPP may be significantly prolonged, potentially causing the tracker to converge on a local maximum power point (LMPP). To address the issues mentioned above, this paper proposes a novel MPPT technique based on the nature-inspired idols algorithm (IA). The technique allows the promotion value (PM) to be adjusted through the anti-fans weight (afw) in the iteration formula, thereby achieving global maximum power point (GMPP) tracking for PVMAs. To verify the effectiveness of the proposed algorithm, a model of a 4-series–3-parallel PVMA was first established using MATLAB (2024b version) software under both non-shading and partial shading conditions. The voltage and current of the PVMAs were fed back, and the IA was then applied for GMPP tracking. The simulation results demonstrate that the IA proposed in this study outperforms existing MPPT techniques, such as particle swarm optimization (PSO), cat swarm optimization (CSO), and the bat algorithm (BA), in terms of tracking speed, dynamic response, and steady-state performance, especially when the array is subjected to varying shading ratios and sudden changes in solar irradiance. Full article

A significant component of making intelligent decisions is optimizing algorithms. In this context, it is imperative to develop algorithms that are more efficient in order to efficiently and accurately process large quantities of intricate data. In addition, the main contribution of this study lies in the integration of optimization theory with swarm intelligence through multicriteria decision-making methods (MCDMs). This study indicates that combining dimensional analysis (DA) with particle swarm optimization (PSO) can smartly and efficiently improve analysis and decision making, resolving PSO’s shortcomings. A convergence investigation between the bat algorithm (BA), MOORA-PSO, TOPSIS-PSO, DA-PSO, and PSO is carried out to substantiate this assertion. Additionally, the ANOVA method is used to validate data dependability in order to evaluate the algorithms’ correctness. Full article

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, and emergent social dynamics. These dynamics include: (1) attraction between similar particles, (2) formation of stable particle clusters, (3) division of groups upon reaching a critical size, (4) inter-group interactions that influence particle distribution during the search process, and (5) internal state changes in particles driven by local interactions. The model’s versatility, including cross-group monitoring and adaptability to environmental interactions, makes it a powerful tool for exploring diverse scenarios. GSM is rigorously evaluated against established and recent metaheuristic algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), Bat Algorithm (BA), Artificial Bee Colony (ABC), Artificial Hummingbird Algorithm (AHA), AHA with Aquila Optimization (AHA-AO), Colliding Bodies Optimization (CBO), Enhanced CBO (ECBO), and Social Network Search (SNS). Performance is assessed using 22 benchmark functions, demonstrating GSM’s competitiveness. Additionally, GSM’s efficiency in image thresholding segmentation is highlighted, as it achieves high-quality results with fewer iterations and particles compared to other methods. Full article

The Yungang Grottoes are undergoing degradation by weather and environmental erosion. Here, we propose a natural ventilation strategy to optimize the environments in Cave 9 and Cave 10 of the Yungang Grottoes. The novelty of this work is to use an effective computational fluid dynamics (CFD) simulation and a hybrid of the beetle antennae search and particle swarm optimization algorithms (BAS–PSO) to determine which natural ventilation scenario yields the maximum total heat transfer rate (Q_max). A CFD hygrothermal model is first developed and shows high precision in predicting temperature and humidity conditions based on real-time measured data. The natural ventilation efficiency is enhanced by different configurations of doors and windows with four ventilation rates. Combined with eXtreme Gradient Boosting (XGBoost) fitting, the hybrid BAS–PSO algorithm yields the largest Q_max (5746.74 W), which is further confirmed by CFD simulations with the outcome of a comparable Q_max (5730.67 W). It indicates that the hybrid algorithm exhibits a good performance in the identification of optimal configurations. The effectiveness of the proposed natural ventilation strategy is verified by on-site measured data. Our findings provide an effective natural ventilation strategy that is beneficial to the energy-efficient preservation of the Yungang Grottoes. Full article

In this study, a hybrid heading control framework for unmanned surface vehicles (USVs) is proposed, combining variable domain fuzzy Proportional–Integral–Derivative (VUF-PID) with an improved algorithmic Beetle Antennae Search–Particle Swarm Optimization–Simulated Annealing (BAS-PSO-SA) optimization to address the multi-objective control challenge. Key innovations include a self-tuning VUF mechanism that improves disturbance rejection by 42%, a weighted adaptive optimization strategy that reduces parameter tuning iterations by 37%, and an asymmetric learning factor that balances global exploration and local refinement. Benchmarks using Rastrigin, Griewank, and Sphere functions show superior convergence and 68% stability improvement. Ocean heading simulations of a 7.02 m unmanned surface vehicle (USV) using the Nomoto model show a 91.7% reduction in stabilization time, a 0.9% reduction in overshoot, and a 30% reduction in optimization iterations. The experimental validation under wind and wave disturbances shows that the heading deviation is less than 0.0392°, meeting the IMO MSC.1/Circ.1580 standard, and an 89.5% improvement in energy efficiency. Although the processing time is 12.7% longer compared to the GRO approach, this framework lays a solid foundation for ship autonomy systems, and future enhancements will focus on MPC-based time delay compensation and Field-Programmable Gate Array (FPGA) acceleration. Full article

Coal mine safety is vital not only for maintaining production operations but also for ensuring the industry’s sustainable development. The threat posed by mine water hazards is especially severe, growing more critical as mining activities become more intense and reach greater depths. Currently, common methods for identifying water sources mainly depend on hydrochemical data, supplemented by analyses of water level and temperature changes. However, due to constraints in cost, time, and the complexity of mining conditions, there is still significant potential for enhancing water source identification techniques. To advance water source identification, this study introduces a novel approach that uses a spectrophotometer to gather spectral data from water sources. These data are then integrated with a bat algorithm (BA)-optimized radial basis function (RBF) neural network to develop a model for identifying water inrush sources. At Baode Coal Mine in China, 105 water samples from four different sources were collected and analyzed using spectral data. The baseline was corrected using the second derivative technique to ensure the data’s integrity. Additionally, 54 sets of historical hydrochemical data were collected for comparison with the spectral data-based model. Theoretical analysis and experimental results show that both hydrochemical and spectral data are effective for identifying water inrush sources. The hydrochemical data model achieved an accuracy of about 90%, whereas the model based on spectral data reached an average accuracy of 95%. Among the tested models: RBF, GA-RBF, PSO-RBF, BA-RBF, and the BA-RBF model demonstrated superior performance, providing the most rapid and accurate identification of water inrush. Full article

In emergency rescue scenarios, drones can be equipped with different payloads as needed to aid in tasks such as disaster reconnaissance, situational awareness, communication support, and material assistance. However, rescue missions typically face challenges such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance. To address these issues, an unmanned emergency support system tailored for emergency rescue scenarios is designed. This system leverages 5G edge computing technology to provide high-speed and flexible network access along with elastic computing power support, reducing the complexity of data fusion across heterogeneous networks. It supports the control and data transmission of drones through the separation of the control plane and the data plane. Furthermore, by applying the Tammer decomposition method to break down the system optimization problem, the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed to jointly optimize the system’s energy consumption and latency. Through simulation experiments, the GLSOAG demonstrates significant advantages over the Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO), and Beetle Antennae Search Algorithm (BAS) in terms of convergence speed, optimization accuracy, and stability. The system optimization approach effectively reduces the system’s energy consumption and latency costs. Overall, our work alleviates the pain points faced in rescue scenarios to some extent. Full article

The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot’s trajectory. The optimization process adjusts the robot’s coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot’s trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments. Full article

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data. Full article

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. Full article

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness. Full article