Search Results (50)

In the heating, ventilation, and air conditioning (HVAC) systems of mushroom growing control rooms, traditional rule-based control methods are commonly adopted. However, these methods are characterized by response delays, leading to underutilization of energy-saving potential and energy costs that constitute a disproportionately high share of overall production costs. Therefore, minimizing the running time of the air conditioning system is crucial while maintaining the optimal growing environment for mushrooms. To address the aforementioned issues, this paper proposed a sensor optimization method based on the combination of principal component analysis (PCA) and information entropy. Furthermore, model predictive control (MPC) was implemented using a gated recurrent unit (GRU) neural network with an attention mechanism (GRU-Attention) as the prediction model to optimize the air conditioning system. First, a method combining PCA and information entropy was proposed to select the three most representative sensors from the 16 sensors in the mushroom room, thus eliminating redundant information and correlations. Then, a temperature prediction model based on GRU-Attention was adopted, with its hyperparameters optimized using the Optuna framework. Finally, an improved crayfish optimization algorithm (ICOA) was proposed as an optimizer for MPC. Its objective was to solve the control sequence with high accuracy and low energy consumption. The average energy consumption was reduced by approximately 11.2%, achieving a more stable temperature control effect. Full article

This paper introduces an effective approach for rotatory fault diagnosis, specifically focusing on centrifugal pumps, by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature-level integration. Centrifugal pumps are critical in various industries, and their condition monitoring is essential for reliability. The proposed methodology addresses the limitations of traditional single-sensor fault diagnosis by fusing information from acoustic and vibration signals. CEEMDAN was employed to decompose raw signals into intrinsic mode functions (IMFs), mitigating noise and non-stationary characteristics. Weighted kurtosis was used to select significant IMFs, and a comprehensive set of time, frequency, and time–frequency domain features was extracted. Feature-level fusion integrated these features, and a support vector machine (SVM) classifier, optimized using the crayfish optimization algorithm (COA), identified different health conditions. The methodology was validated on a centrifugal pump with various impeller defects, achieving a classification accuracy of 95.0%. The results demonstrate the efficacy of the proposed approach in accurately diagnosing the state of centrifugal pumps. Full article

In this paper, a pattern synthesis method of uniform linear and concentric elliptical antenna arrays using the Kepler optimization algorithm (KOA) is proposed. The KOA, which utilizes Kepler’s laws to predict the position and velocity of planets at arbitrary times, is first applied to deal with the optimization problems of linear and elliptical antenna arrays. Radiation patterns with high gain and low sidelobe levels (SLLs) are synthesized by optimizing the critical parameters (amplitude, phase, and rotation) of the linear arrays. Moreover, a concentric elliptical array is designed to demonstrate the capability of the KOA framework to solve complex problems and achieve the desired performance. In order to accurately consider mutual coupling between the elements, the full-wave method of moments (MoM) is used to calculate the radiation characteristics of the arrays in the optimization method. The effectiveness of the proposed method is proved by four typical examples. The results show that, compared with the butterfly optimization algorithm (BOA), Harris hawks optimization (HHO), and crayfish optimization algorithm (COA), the proposed method possesses high gain and SLL suppression capabilities, which makes it suitable for various array types. Full article

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity. Full article

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish (COA) algorithm to adaptively obtain the optimal parameter combination of the number of modal decompositions and quadratic penalty factors for VMD, and then, sample entropy is used to identify effective mode components (IMF), which are reconstructed into denoised signals to achieve effective separation of signal and noise The experiments were conducted using simulated signals and 52 GPS station data from CMONOC to compare and analyze the COA–VMPE–WD method with wavelet denoising (WD), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The result shows that the COA–VMPE–WD method can effectively remove noise from GNSS coordinate time series and preserve the original features of the signal, with the most significant effect on the U component. The COA–VMPE–WD method reduced station velocity by an average of 50.00%, 59.09%, 18.18%, and 64.00% compared to the WD, EMD, EEMD, and CEEMDAN methods. The noise reduction effect is higher than the other four methods, providing reliable data for subsequent analysis and processing. Full article

The red-claw crayfish Cherax quadricarinatus has been widely introduced and cultured in China and has become a crucial economic freshwater species. However, individuals reared from the same batch of seedlings in uniform aquaculture systems exhibit significant size variation within and between genders, which notably impedes the optimization of both their quality and yield. Gut microbiota plays an important role in the metabolism, development, and immunity of aquatic animals. However, the knowledge on the intestinal microbiota of red-claw crayfish with various sizes and genders is poor. In this study, the intestinal microbiota of red-claw crayfish cultured in consistent farming conditions were separated to larger-sized female (GUBF), larger-sized male (GUBM), smaller-sized female (GUSF), and smaller-sized male (GUSM) groups based on their body size (weight) and gender, before being analyzed via high-throughput 16S rRNA gene sequencing. The intestinal microbiota results showed that alpha diversity tended to generally decrease in the order of GUBF, GUBM, GUSF, and GUSM, indicating that the richness and evenness of the gut flora were gradually improved with the increase in body weight or from male to female. Community richness and diversity were highest in the GUBF group, followed by the GUBM, GUSF, and GUSM groups, respectively. Beta diversity indicated significant differences in gut microbiota between the GUBF and GUSF, GUBM and GUSM, GUBF, and GUBM groups. Further analysis showed that the dominant phyla in the intestine of the red-claw crayfish were Firmicutes, Proteobacteria, Fusobacteriota, Bacteroidota, and Deinococcota, and the dominant genera were Vibrio, Tyzzerella, Candidatus Bacilloplasma, Citrobacter, and Candidatus Hepatoplasma. Moreover, nine phyla and 106 genera were identified to be significantly different in abundance among all four groups. Pairwise comparisons revealed that the phylum Dependentiae and Planctomycetota and genus Babeliaceae_unclassified were significantly abundant in the gut of female crayfishes, regardless of body size. On the other hand, irrespective of genders, the abundance of Novosphingobium, Piscinibacter, and Citrobacter was significantly increased or declined in the larger or smaller crayfishes, respectively. PICRUSt2 analysis based on the KEGG database suggested that the pathway bacterial secretion system, isoflavonoid biosynthesis, and pathway glycerolipid metabolism were significantly up- and down-regulated in female individuals, respectively, regardless of body sizes. Meanwhile, the adipocytokine signaling pathway, pyruvate metabolism, and pathway electron transfer carriers were significantly up- and down-regulated in larger individuals, respectively, regardless of gender. Gender differences may induce gut microbiota to exert a greater impact on hormonal regulation, whereas differences in individual size seem to lead gut microbiota to develop a preference for food intake and energy sources. In summary, this study revealed key differences in the intestinal microbiota of the crayfish with different sizes and genders, even in those which were cultured in the same environment and period, which potentially suggest that the intestinal microbiota may be influenced by some other factors in the culture system, such as hormone secretion, metabolism, and immunity. This study will contribute to improving growth performance and animal quality in the aquaculture of C. quadricarinatus. Full article

This study investigated the effects of dietary Rhodotorula mucilaginosa supplementation with different concentrations (0.0 g/kg, 0.1 g/kg, 1.0 g/kg, 10.0 g/kg) on red claw crayfish (Cherax quadricarinatus). Four groups were established: control group (CK, 0.0 g/kg), low-dose group (HL, 0.1 g/kg), medium-dose group (HM, 1.0 g/kg), and high-dose group (HH, 10.0 g/kg). The feeding trial lasted for 56 days. The results showed that, compared with the control group, all supplementation groups exhibited significantly reduced feed conversion ratios (p < 0.05). The HM and HH groups demonstrated significant increases in body length growth rate, specific growth rate, weight gain rate, hepatosomatic index, and survival rate (p < 0.05). All supplemented groups showed significantly enhanced trypsin and lipase activities in intestines and trypsin activity in the hepatopancreas (p < 0.05). The HM and HH groups exhibited elevated α-amylase activity in the hepatopancreas (p < 0.05). Compared with the control group, marine red yeast supplementation reduced colonization of potential pathogens while increasing probiotic abundance, effectively improving intestinal microbiota structure. The HM group significantly improved intestinal villus length, width, and muscular thickness (p < 0.05). All supplemented groups showed considerable upregulation of hepatopancreatic genes related to immunity (heat shock protein 70, down syndrome cell adhesion molecule, crustacean antibacterial peptide, serine proteinase inhibitors, crustacean hyperglycemic hormone, anti-lipopolysaccharide factor, lysozyme, and alkaline phosphatase) and antioxidant defense (superoxide dismutase, glutathione peroxidase, glutathione, and catalase) (p < 0.05). These findings indicate that R. mucilaginosa can significantly enhance digestive enzyme activity, maintain intestinal health, improve antioxidant and immune-related gene expression, and promote growth performance in red claw crayfish, with the HM group (1.0 g/kg R. mucilaginosa) showing optimal promotion effects. Full article

The widespread application of unmanned vehicles in logistics distribution and special transportation has made improving trajectory tracking accuracy and dynamic adaptability critical for operational efficiency. This article proposes a biologically inspired combination control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by the Crayfish Optimization Algorithm (COA) to address limitations in generalization and dynamic adaptability. The proposed DDPG-COA controller embodies a symmetrical structure: DDPG acts as the primary controller for global trajectory tracking, while COA serves as a compensatory regulator, dynamically optimizing actions through a disturbance observation mechanism. This symmetrical balance between learning-based control (DDPG) and bio-inspired optimization (COA) ensures robust performance in complex scenarios. Experiments on symmetrical trajectories demonstrated significant improvements, with the average tracking errors reduced by 56.3 percent, 71.6 percent, and 74.6 percent, respectively. The results highlight how symmetry in control architecture and trajectory design synergistically enhances precision and adaptability for unmanned systems. Full article

This study investigated the effects of dietary supplementation with varying levels (CK: 0.0 g/kg; RL: 0.1 g/kg; RM: 1.0 g/kg; RH: 10.0 g/kg) of Rhodotorula mucilaginosa on muscle composition, serum biochemical indicators, antioxidant capacity, lipid metabolism, and the mTOR signaling pathway in red claw crayfish (Cherax quadricarinatus). Results showed that, compared to CK, treatment groups had higher muscle crude protein, fat, leucine, histidine, arginine, and essential amino acids (p < 0.05), and lower saturated fatty acids (p < 0.05). Treatment groups also exhibited increased activities of alkaline phosphatase, acid phosphatase, superoxide dismutase, catalase, glutathione S-transferase, lysozyme, albumin, total protein, and antioxidant capacity (p < 0.05), with reduced activities of aspartate aminotransferase, alanine aminotransferase, lactate dehydrogenase, and triglycerides (p < 0.05). In the hepatopancreas, treatment groups showed significant downregulation of AMP-activated protein kinase α, β, and γ, and carnitine palmitoyltransferase 1 genes (p < 0.05). Conversely, genes involved in lipid anabolism (peroxisome proliferator-activated receptor γ, acetyl-CoA carboxylase, fatty acid synthase, sterol regulatory element-binding protein, protein kinase B, and mammalian target of rapamycin 1 and 2) were upregulated (p < 0.05). In conclusion, R. mucilaginosa supplementation affects muscle composition, lipid metabolism, and mTOR signaling. The optimal dose is 1.0 g/kg. Full article

Reliable short-term electricity usage prediction is essential for preserving the stability of topologically symmetric power networks and their dynamic supply–demand equilibrium. To tackle this challenge, this paper proposes a novel approach derived from the standard Extreme Learning Machine (ELM) by integrating an enhanced Crayfish Optimization Algorithm (DSYCOA). This algorithm combines Logistic chaotic mapping, local precise search, and dynamic parameter adjustment strategies designed to achieve a dynamic balance between exploration and exploitation, thereby optimizing the initial thresholds and weights of the ELM. Consequently, a new short-term power load forecasting model, namely the DSYCOA-ELM model, is developed. Experimental validation demonstrates that the improved DSYCOA exhibits fast convergence speed and high convergence accuracy, and successfully harmonizes global exploration and local exploitation capabilities while maintaining an empirical balance between exploration and exploitation. To additionally verify the effectiveness of DSYCOA in improving ELM, this paper conducts simulation comparison experiments among six models, including DSYCOA-ELM, ELM, and ELM improved by BWO (BWO-ELM). The findings demonstrate that the DSYCOA-ELM model outperforms the other five forecasting models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other indicators. Specifically, in terms of MAPE, DSYCOA-ELM reduces the error by 96.9% compared to ELM. This model demonstrates feasibility and effectiveness in solving the problem of short-term power load prediction, providing critical support for maintaining the stability of grid topological symmetry and supply–demand balance. Full article

To enhance the rapid and stable tracking of a specified trajectory by quadcopter drones, while ensuring a degree of resistance to external wind disturbances, this paper proposes an integrated control strategy that combines an optimization algorithm and fuzzy control. In this system, both the position and attitude loops utilize second-order Linear Active Disturbance Rejection Control (LADRC) controllers, supplemented by fuzzy controllers. These controllers have been optimized using a modified crayfish optimization algorithm (MCOA), resulting in a dual-closed-loop control system. In comparisons with both the dual-closed-loop LADRC controller and the dual-closed-loop fuzzy control LADRC controller, the proposed method reduces the rise time by 52.87% in the X-channel under wind-free conditions, reduces the maximum trajectory tracking error by 86.37% under wind-disturbed conditions, and reduces the ITAE exponent by 66.2%, which demonstrates that the newly designed system delivers excellent tracking speed and accuracy along the specified trajectory. Furthermore, it remains effective even in the presence of external disturbances, it can reliably maintain the target position and the attitude angle, demonstrating strong resistance to interference and stability. Full article

The Crayfish Optimization Algorithm (COA) has limitations that affect its optimization performance seriously. The competition stage of the COA uses a simplified mathematical model that concentrates on relations of distance between crayfish only. It is deprived of a stochastic variable and is not able to generate an applicable balance between exploration and exploitation. Such a case causes the COA to have early convergence, to perform poorly in high-dimensional problems, and to be trapped by local minima. Moreover, the low activation probability of the summer resort stage decreases the exploration ability more and slows down the speed of convergence. In order to compensate these shortcomings, this study proposes an Improved Crayfish Optimization Algorithm (ICOA) that designs the competition stage with three modifications: (1) adaptive step length mechanism inversely proportional to the number of iterations, which enables exploration in early iterations and exploitation in later stages, (2) vector mapping that increases stochastic behavior and improves efficiency in high-dimensional spaces, (3) removing the X_shade parameter in order to abstain from early convergence. The proposed ICOA is compared to 12 recent meta-heuristic algorithms by using the CEC-2014 benchmark set (30 functions, 10 and 30 dimensions), five engineering design problems, and a real-world ROAS optimization case. Wilcoxon Signed-Rank Test, t-test, and Friedman rank indicate the high performance of the ICOA as it solves 24 of the 30 benchmark functions successfully. In engineering applications, the ICOA achieved an optimal weight (1.339965 kg) in cantilever beam design, a maximum load capacity (85,547.81 N) in rolling element bearing design, and the highest performance (144.601) in ROAS optimization. The superior performance of the ICOA compared to the COA is proven by the following quantitative data: 0.0007% weight reduction in cantilevers design (from 1.339974 kg to 1.339965 kg), 0.09% load capacity increase in bearing design (COA: 84,196.96 N, ICOA: 85,498.38 N average), 0.27% performance improvement in ROAS problem (COA: 144.072, ICOA: 144.601), and most importantly, there seems to be an overall performance improvement as the COA has a 4.13 average rank while the ICOA has 1.70 on CEC-2014 benchmark tests. Results indicate that the improved COA enhances exploration and successfully solves challenging problems, demonstrating its effectiveness in various optimization scenarios. Full article

In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs. Full article

In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolution, named the ICOA, is proposed. First, the elite chaotic difference strategy is used for population initialization to generate a more uniform crayfish population and increase the quality and diversity of the population. Secondly, the differential evolution strategy and the dimensional variation strategy are introduced to improve the quality of the crayfish population before its iteration and to improve the accuracy of the optimal solution and the local search ability for crayfish at the same time. To enhance the updating approach to crayfish exploration, the Levy flight strategy is adopted. This strategy aims to improve the algorithm’s search range and local search capability, prevent premature convergence, and enhance population stability. Finally, the adaptive parameter strategy is introduced to improve the development stage of crayfish, so as to better balance the global search and local mining ability of the algorithm, and to further enhance the optimization ability of the algorithm, and the ability to jump out of the local optimal. In addition, a comparison with the original COA and two sets of optimization algorithms on the CEC2019, CEC2020, and CEC2022 test sets was verified by Wilcoxon rank sum test. The results show that the proposed ICOA has strong competition. At the same time, the performance of ICOA is tested against different high-performance algorithms on 6 engineering optimization examples, 30 high–low-dimension constraint problems and 2 large-scale NP problems. Numerical experiments results show that ICOA has superior performance on a range of engineering problems and exhibits excellent performance in solving complex optimization problems. Full article

This paper proposes a Kalman filter-based cascaded PI-(1+PI) controller, optimized using an Improved Spider Wasp Optimizer (ISWO), to address the challenges of USV heading control in dynamic marine environments. Traditional PID controllers struggle with nonlinearities and noise in USV systems while existing metaheuristic algorithms face limitations in balancing exploration and exploitation. To overcome these issues, the ISWO integrates dynamic adaptive grouping, perturbation dimension-symmetric distance optimization, and nonlinear time-varying weights, enhancing convergence speed and optimization accuracy. A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). The proposed KF-PI(1+PI) controller incorporates a Kalman filter to suppress noise and a cascaded structure to improve gain and response speed, reducing integrated time absolute error (ITAE) by 84% relative to traditional PID controllers. The hardware-in-the-loop simulation experiments further validate the proposed controller’s robustness. The study demonstrates that ISWO-optimized control systems significantly enhance USV navigation precision and adaptability, offering a viable solution for autonomous marine operations. Full article