Search Results (26)

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

Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO’s search efficiency and spatial exploration, this study introduces Sine–Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. Full article

This paper presents two novel hybrid iterative schemes that combine Newton’s method and its variant with the Tuna Swarm Optimization (TSO) algorithm, aimed at solving complex nonlinear equations with enhanced accuracy and efficiency. Newton’s method is renowned for its rapid convergence in root-finding problems, and it is integrated with TSO, a recent swarm intelligence algorithm that surpasses the complex behavior of tuna fish in order to optimize the search for superior solutions. These hybrid methods are reliable and efficient for solving challenging mathematical and applied science problems. Several numerical experiments and applications involving ordinary differential equations have been carried out to demonstrate the superiority of the proposed hybrid methods in terms of convergence rate, accuracy, and robustness compared to traditional optimization and iterative methods. The stability and efficiency of the proposed methods have also been verified. The results indicate that the hybrid approaches outperform traditional methods, making them a promising tool for solving a wide range of mathematical and engineering problems. Full article

In resource-constrained wireless sensor networks, efficient link scheduling is a well-studied challenge. This problem is NP-hard, indicating that NP (Nondeterministic Polynomial Time) refers to problems whose solutions can be verified in polynomial time but are computationally difficult to find, and traditional methods seldom yield optimal solutions within practical time limits. This research introduces an innovative novel link scheduling strategy based on the Tuna Swarm Optimization (TSO-LS) algorithm to optimize the link scheduling performance of wireless sensor networks. This work enhances the tuna swarm algorithm’s search process by incorporating characteristics of the link scheduling problem, resulting in specialized algorithmic improvements for this scenario. This research presents three principal improvements to the algorithm: first, optimizing the individual update mechanism to expedite scheduling solutions; second, refining the leading individual selection strategy to elevate global scheduling quality; and third, maintaining population diversity to prevent convergence on suboptimal scheduling schemes. In the experimental section, TSO-LS is compared with the Genetic Algorithm, Particle Swarm Optimization, Enhanced Particle Swarm Optimization and Ant Colony Optimization. The results show that TSO-LS achieves a 13.3% improvement in energy efficiency and a 12.5% decrease in average latency. Under different experimental conditions, the TSO-LS strategy shortens the average latency to 10.5 ms, demonstrating outstanding overall performance. Furthermore, this strategy reduces node consumption from 0.41 mJ to 0.32 mJ, significantly extending the overall lifespan of the network. Full article

Language is that kind of expression by which effective communication with another can be well expressed. One may consider such as a connecting bridge for bridging communication gaps for the hearing- and speech-impaired, even though it remains as an advanced method for hand gesture expression along with identification through the various different unidentified signals to configure their palms. This challenge can be met with a novel Enhanced Convolutional Transformer with Adaptive Tuna Swarm Optimization (ECT-ATSO) recognition framework proposed for double-handed sign language. In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. Feature graining is employed to obtain local features, and the ViT transformer architecture is then utilized to capture global features from the preprocessed images. After concatenation, this generates a feature map that is then divided into various words using an Inverted Residual Feed-Forward Network (IRFFN). Using the Tuna Swarm Optimization (TSO) algorithm in its enhanced form, the provided Enhanced Convolutional Transformer (ECT) model is optimally tuned to handle the problem dimensions with convergence problem parameters. In order to solve local optimization constraints when adjusting the position for the tuna update process, a mutation operator was introduced. The dataset visualization that demonstrates the best effectiveness compared to alternative cutting-edge methods, recognition accuracy, and convergences serves as a means to measure performance of this suggested framework. Full article

To achieve time–energy–impact multi-objective optimization in the trajectory control of underwater manipulators, this paper proposes a Fast Non-Dominated Sorting Tuna Swarm Optimization algorithm (FNS-TSO). The algorithm integrates a fast non-dominated sorting mechanism into the Tuna Swarm Optimization algorithm, improves initialization through Optimal Latin Hypercubic Sampling (OLHS) to enhance population distribution uniformity, and incorporates a nonlinear dynamic weight to refine the spiral foraging strategy, thereby strengthening algorithmic robustness. To verify FNS-TSO’s effectiveness, we conducted comparative evaluations using standard test functions against three established algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Jellyfish Search Optimization (MOJSO), and the Non-Dominated-Sorting Genetic Algorithm (NSGA-II). Results demonstrate superior overall performance, particularly regarding convergence speed and solution diversity, with solution set distributions showing enhanced uniformity. In practical implementation, we applied FNS-TSO to the multi-objective optimization of an underwater manipulator using quintic spline curves for trajectory planning. Simulation outcomes reveal respective reductions of 11.03% in total operation time, 19.02% in energy consumption, and 24.69% in mechanical impacts, with the optimized manipulator achieving stable point-to-point motion transitions. Full article

The accurate estimation of reference crop evapotranspiration (ET₀) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET₀ estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R² and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d⁻¹, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (T_max), minimum temperature (T_min), total solar radiation (R_s), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U₂) in the temperate continental climate region (TCC)—which was the largest variable affecting ET₀—n, R_a, and total solar radiation (R_s) in the other climate regions were more important than relative humidity (RH) and wind speed (U₂) in predicting ET₀. Therefore, AO-ELM4 was selected for the TCC region (with T_max, T_min, R_s, and U₂ as inputs) and AO-ELM5 (with T_max, T_min, R_s, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data. Full article

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation. Full article

Hydropower units are integral to sustainable energy production, and their operational reliability hinges on accurate status prediction. This paper introduces an innovative hybrid deep learning model that synergistically integrates a Temporal Convolutional Network (TCN), a Residual Short-Term LSTM (REST-LSTM) network, a Gated Recurrent Unit (GRU) network, and the tuna swarm optimization (TSO) algorithm. The model was meticulously designed to capture and utilize temporal features inherent in time series data, thereby enhancing predictive performance. Specifically, the TCN effectively extracts critical temporal features, while the REST-LSTM, with its residual connections, improves the retention of short-term memory in sequence data. The parallel incorporation of GRU further refines temporal dynamics, ensuring comprehensive feature capture. The TSO algorithm was employed to optimize the model’s parameters, leading to superior performance. The model’s efficacy was empirically validated using three datasets—unit flow rate, guide vane opening, and maximum guide vane water temperature—sourced from the Huadian Electric Power Research Institute. The experimental results demonstrate that the proposed model significantly reduces both the maximum and average prediction errors, while also offering substantial improvements in forecasting accuracy compared with the existing methodologies. This research presents a robust framework for hydropower unit operation prediction, advancing the application of deep learning in the hydropower sector. Full article

The reliability and stability of marine diesel engines are pivotal to the safety and economy of maritime operations. Accurate and efficient prediction of the states of these engines is essential for performance evaluation and operational continuity. This paper introduces a novel hybrid deep learning model, the multi-dimensional global temporal predictive (MDGTP) model, designed for synchronous multi-state prediction of marine diesel engines. The model incorporates parallel multi-head attention mechanisms, an enhanced long short-term memory (LSTM) with interleaved residual connections, and gated recurrent units (GRUs). Additionally, we propose a dynamic arithmetic tuna optimization algorithm, which synergizes tuna swarm optimization (TSO), and the arithmetic optimization algorithm (AOA) for hyperparameter optimization, thereby enhancing prediction accuracy. Comparative experiments using actual marine diesel engine data demonstrate that our model outperforms the LSTM, GRU, LSTM–GRU, support vector regression (SVR), random forest (RF), Gaussian process regression (GPR), and back propagation (BP) models, achieving the lowest root mean squared error (RMSE) and mean absolute error (MAE), as well as the highest Pearson correlation coefficient across three sampling periods. Ablation studies confirm the significance of each component in improving prediction accuracy. Our findings validate the efficacy of the proposed MDGTP model for predicting the multi-dimensional operating states of marine diesel engines. Full article

The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm’s capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications. Full article

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions. Full article

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm² single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated. Full article

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference. Full article

To overcome the shortcoming of the Fuzzy C-means algorithm (FCM)—that it is easy to fall into local optima due to the dependence of sub-spatial clustering on initialization—a Multi-Strategy Tuna Swarm Optimization-Fuzzy C-means (MSTSO-FCM) algorithm is proposed. Firstly, a chaotic local search strategy and an offset distribution estimation strategy algorithm are proposed to improve the performance, enhance the population diversity of the Tuna Swarm Optimization (TSO) algorithm, and avoid falling into local optima. Secondly, the search and development characteristics of the MSTSO algorithm are introduced into the fuzzy matrix of Fuzzy C-means (FCM), which overcomes the defects of poor global searchability and sensitive initialization. Not only has the searchability of the Multi-Strategy Tuna Swarm Optimization algorithm been employed, but the fuzzy mathematical ideas of FCM have been retained, to improve the clustering accuracy, stability, and accuracy of the FCM algorithm. Finally, two sets of artificial datasets and multiple sets of the University of California Irvine (UCI) datasets are used to do the testing, and four indicators are introduced for evaluation. The results show that the MSTSO-FCM algorithm has better convergence speed than the Tuna Swarm Optimization Fuzzy C-means (TSO-FCM) algorithm, and its accuracies in the heart, liver, and iris datasets are 89.46%, 63.58%, 98.67%, respectively, which is an outstanding improvement. Full article