Search Results (20)

The basic Salp Swarm Algorithm (SSA) offers advantages such as a simple structure and few parameters. However, it is prone to falling into local optima and remains inadequate for seed classification tasks that involve hyperparameter optimization of machine learning classifiers such as Support Vector Machines (SVMs). To overcome these limitations, an Enhanced Knowledge-based Salp Swarm Algorithm (EKSSA) is proposed. The EKSSA incorporates three key strategies: Adaptive adjustment mechanisms for parameters $c_1$ and $\alpha$ to better balance exploration and exploitation within the salp population; a Gaussian walk-based position update strategy after the initial update phase, enhancing the global search ability of individuals; and a dynamic mirror learning strategy that expands the search domain through solution mirroring, thereby strengthening local search capability. The proposed algorithm was evaluated on thirty-two CEC benchmark functions, where it demonstrated superior performance compared to eight state-of-the-art algorithms, including Randomized Particle Swarm Optimizer (RPSO), Grey Wolf Optimizer (GWO), Archimedes Optimization Algorithm (AOA), Hybrid Particle Swarm Butterfly Algorithm (HPSBA), Aquila Optimizer (AO), Honey Badger Algorithm (HBA), Salp Swarm Algorithm (SSA), and Sine–Cosine Quantum Salp Swarm Algorithm (SCQSSA). Furthermore, an EKSSA-SVM hybrid classifier was developed for seed classification, achieving higher classification accuracy. Full article

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

This study proposes a novel control framework for semi-active seat suspensions, specifically targeting motion sickness mitigation through precision suppression of vertical vibrations within the 0.1–0.5 Hz frequency range. Firstly, a fractional-order band-stop filter in conjunction with a linear quadratic regulator (LQR) controller under frequency-domain sensitivity constraints (0.1–0.5 Hz) is proposed to achieve frequency-selective vibration attenuation. Secondly, the multi-objective butterfly optimization algorithm (MOBOA) is adopted to optimize the LQR controller’s weighting matrices (Q, R) by balancing conflicting requirements in terms of human body displacement limits, acceleration thresholds, and suspension travel. Finally, experimental validation under concrete pavement excitation and random road profiles demonstrates significant advantages over conventional LQR, i.e., a 41.04% reduction in vertical vibration amplitude and a 55.95% suppression of acceleration peaks within the target frequency band. The combined enhancements offer dual benefits of enhancing ride comfort and motion sickness mitigation in real-world driving scenarios. Full article

Selecting appropriate locations of emergency centers is an important issue in avoiding probable damages by natural disasters. Emergency rescue sites are constructed to provide emergency supplies swiftly for people in affected areas. Factors of transportation fluency and road damage degrees should be considered, which largely affect rescue efficiency. In order to find appropriate sites accurately, we proposed a redesigned method Variable Butterfly Optimization Algorithm (VBOA), based on the Butterfly Optimization Algorithm, by adding the Variation Operator mechanism to avoid the limitations of local optimum problems present in other optimization algorithms. The Variation Operator effectively combines both global and local search strategies to improve the performance of global searching, and it accelerates the convergence speed of the algorithm. We conducted our experiment on selected candidate sites with multiple optimization methods; the experiment results demonstrate that our proposed method maintains the balance between conditions of coverage area and expenditure. Our proposed method relieved the reliance of local optimum results and achieved better convergence accuracy in our selected samples in comparison with other methods both in initial and later siting phases. Full article

To comprehensively address the interests of both the supply and demand sides within a microgrid, a two-layer optimal scheduling model incorporating demand response was formulated. The upper tier aims to optimize the load profile, focusing on maximizing electricity consumption satisfaction and minimizing user electricity costs. Meanwhile, the lower tier targets the optimization of output from each controllable generation unit, with the goal of reducing operational costs. Given the nonlinear and multi-constrained nature of this model, an improved nutcracker optimization algorithm (INOA) is proposed. This enhancement introduces chaotic sequences into the original nutcracker optimization algorithm (NOA) for population initialization, employs a hybrid butterfly optimization algorithm to enhance the algorithm’s local search capabilities, and integrates dynamic selection adaptive T-distribution for updating individual positions. The solution tests involving INOA, NOA, dung beetle optimizer (DOB), particle swarm optimization (PSO), grey wolf optimization (GWO), and sparrow search algorithm (SSA) were conducted using the CEC2022 intelligent algorithm test suite. Analysis reveals that INOA exhibits superior comprehensive optimization performance compared to other algorithms, validating the effectiveness of the improvements introduced in this paper. Ultimately, a simulation analysis of the microgrid was performed, demonstrating that, despite a 3.58% reduction in user satisfaction, participation in demand response led to a 25.16% decrease in electricity costs and a 5.92% reduction in microgrid operational costs. These findings substantiate the model’s capability to effectively balance the economic interests of both the supply and demand sides within the microgrid. Full article

Legumes enhance pasture health and soil productivity by fixing atmospheric nitrogen and boosting soil microbiota. We investigated the effects of tropical pasture legumes, including butterfly pea (Clitoria ternatea), seca stylo (Stylosanthes scabra), desmanthus (Desmanthus virgatus), lablab (Lablab purpureus), and Wynn cassia (Chamaecrista rotundifolia), on the soil microbial community and buffel grass (Cenchrus ciliaris) gene expression. Additionally, we explored the impact of a phytogenic bioactive product (PHY) in the coculture system. A pot trial using soil enriched with cow paunch compost included four treatments: monoculture of buffel grass and five legume species with and without PHY supplementation and coculture of buffel grass with each legume species with and without PHY supplementation. Actinobacteriota and Firmicutes were the dominant bacterial phyla. Regardless of PHY application, the coculture of buffel grass with legumes positively influenced microbial composition and diversity. Transcriptomic analysis revealed significant gene expression changes in buffel grass shoots and roots, with each legume uniquely affecting nitrogen metabolism. Lablab and Wynn cassia exhibited similarities in modulating metabolic processes, butterfly pea contributed to mycotoxin detoxification, and desmanthus balanced cell death and growth. Seca stylo enhanced root cell growth and regeneration. These findings offer insights for optimizing legume–grass coculture systems, enhancing soil activity and promoting sustainable agriculture. Full article

In this work, micro air vehicles (MAVs) equipped with bio-inspired wings are investigated experimentally in wind tunnel. The starting point is that insects such as dragonflies, butterflies and locusts have wings with rigid tubular elements (corrugation) connected by flexible parts (profiling). So far, it is important to understand the specific aerodynamic effects of corrugation and profiling as applied to conventional wings for the optimization of low-Reynolds-number aerodynamics. The present study, in comparison to previous investigations on the topic, considers whole MAVs rather than isolated wings. A planform with a low aperture-to-chord ratio is employed in order to investigate the interaction between large tip vortices and the flow over the wing surface at large angles of incidence. Comparisons are made by measuring global aerodynamic loads using force balance, specifically drag and lift, and detailed local velocity fields over wing surfaces, by means of particle image velocimetry (PIV). This type of combined global–local investigation allows describing and relating overall MAV performance to detailed high-resolution flow fields. The results indicate that the combination of wing corrugation and profiling gives effective enhancements in performance, around 50%, in comparison to the classical flat-plate configuration. These results are particularly relevant in the framework of low-aspect-ratio MAVs, undergoing beneficial interactions between tip vortices and large-scale separation. Full article

Reliability and efficiency of valves are necessary for precise control and sufficient heat-flow to heat application plants for the integrated energy systems of nuclear power plants (NPPs). Strategic Management Analysis Requirement and Technology (SMART) valves’ ability to control flow and assess environmental parameters stands out for these requirements. Their ability to sustain the downstream flow rate, prevent reverse flow, and maintain pressure in the heat transport loop is much more efficient with the integration of sensors and intelligent algorithms. For assessing valve performance and monitoring, mechanical design and operating conditions are two important parameters. In this study, the butterfly valves of three different sizes are simulated with water and steam using STAR-CCM+ in various flow regimes and positions to analyze performance parameters to strategize an automated control system for efficiently balancing the heat–transport network. Also, flow behavior is studied using velocity and pressure fields for valve–body geometry optimization. It can be observed, through performance parameters, that the valves are suitable for operation between 30° and 90° positions with significantly low loss coefficients and high flow coefficients, and the performance parameters follow a certain pattern in both water and steam flow in each scenario. Full article

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results. Full article

The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO₂ emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool. Full article

The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy. Full article

With the advancement of technology and the rise of the unmanned aerial vehicle industry, the use of drones has grown tremendously. For drones performing near-ground delivery missions, the problem of 3D space-based path planning is particularly important in the autonomous navigation of drones in complex spaces. Therefore, an improved butterfly optimization (BOA-TSAR) algorithm is proposed in this paper to achieve the autonomous pathfinding of drones in 3D space. First, this paper improves the randomness strategy of the initial population generation in the butterfly optimization algorithm (BOA) via the Tent chaotic mapping method, by means of the removal of the short-period property, which balances the equilibrium of the initial solutions generated by the BOA algorithm in the solution space. Secondly, this paper improves the shortcomings of the BOA algorithm in terms of slower convergence, lower accuracy, and the existence of local optimal stagnation when dealing with high-dimensional complex functions via adaptive nonlinear inertia weights, a simulated annealing strategy, and stochasticity mutation with global adaptive features. Finally, this paper proposes an initial population generation strategy, based on the 3D line of sight (LOS) detection method, to further reduce the generation of path interruption points while ensuring the diversity of feasible solutions generated by the BOA algorithm for paths. In this paper, we verify the superior performance of BOA-TSAR by means of simulation experiments. The simulation results show that BOA-TSAR is very competitive among swarm intelligence (SI) algorithms of the same type. At the same time, the BOA-TSAR algorithm achieves the optimal path length measure and smoothness measure in the path-planning experiment. Full article

The deterioration of the urban environment is a problem which has captured the attention of governmental departments and researchers, who are committed to improving the urban environment from the perspective of optimizing urban morphology. Although many researchers have applied computational fluid dynamics (CFD) plug-ins to study the problems of urban ventilation and pollutant accumulation, studies on the reliability and simulation accuracy verification of CFD plug-ins are currently scarce. Therefore, we used three CFD plug-ins based on different architectural design platforms to evaluate and compare their operation difficulty, simulation accuracy, and efficiency through the analysis of the simulation results of urban ventilation. This study complements the reliability validation of CFD plug-in simulations and guides urban planners and architects in the selection and application of CFD plug-ins. The results show that the CFD plug-in generally underestimates the wind speed at the pedestrian level and the prediction accuracy is poor in the wake area of obstacles, especially with the GH_Wind plug-in. Under the 0° inflow direction, the simulation results of the Butterfly plug-in were the most consistent with the experimental values. When the inflow direction increased to 22.5° and 45°, the Autodesk CFD showed the best simulation accuracy. Overall, Autodesk CFD achieves a balance between simulation accuracy and speed in urban airflow simulation. Full article

As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate. Full article