Search Results (28)

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly influenced by the precision of its identified parameters. However, estimating these parameters remains a difficult nonlinear optimization problem, especially under low state of charge (SOC) operation. Classical identification approaches often have limited robustness under such conditions, while metaheuristic algorithms provide a promising alternative because of their ability to handle nonlinear and multimodal search spaces. Even so, many existing methods still encounter drawbacks related to convergence speed and susceptibility to local optima. Motivated by these challenges, this study investigates the recently introduced Artificial Lemming Algorithm (ALA) for parameter identification of a second-order equivalent circuit model (2RC-ECM) under EV-oriented low-SOC operating conditions. Experimental validation is conducted using two independent dynamic datasets, namely the High Dynamic Profile (HDP) at 25 °C and the Urban Dynamometer Driving Schedule (UDDS) at −5 °C, involving different lithium-ion cells and operating conditions. ALA is benchmarked against nine competing metaheuristic algorithms under identical search boundaries and computational settings. Performance is assessed using RMSE, MAE, MaxAE, bias, convergence behavior, error distributions, execution time, and sensitivity to the number of independent runs, population size, and maximum number of iterations. The results show that ALA achieves the lowest minimum, mean, and maximum RMSE for both datasets, with minimum RMSE values of $0.01075 \mathrm{V}$ for HDP and $0.03534 \mathrm{V}$ for UDDS. Unseen-data validation further yields RMSE and MAE values of $0.0082$ and $0.0061 \mathrm{V}$, respectively, for HDP, and $0.0416$ and $0.0299 \mathrm{V}$, respectively, for UDDS. In addition, convergence, error-distribution, and sensitivity analyses show that ALA maintains competitive and consistent performance across the investigated configurations. Overall, the results demonstrate that ALA provides a favorable balance between estimation accuracy, robustness, convergence behavior, and computational cost for offline lithium-ion battery parameter identification. Full article

Robotic multi-seam welding path planning for box-type thin-walled structures faces significant challenges due to multiple constraints, multiple objectives, and its high-dimensional discrete combinatorial nature. To address these issues, a deformation-informed multi-objective path planning method for multi-seam welding (DMOPP) is proposed, comprising a multi-objective formulation and an optimization algorithm. At the modeling level, welding path length and maximum structural deformation are defined as the optimization objectives. Collision-free path planning is used to calculate the path length, while an XGBoost-based surrogate model is developed to establish the mapping between welding path variables and maximum deformation, enabling rapid deformation prediction without computationally expensive finite element simulations. At the optimization level, a modified discrete artificial lemming algorithm (MODALA) is proposed to improve global search capability and convergence stability. Experimental results show that MODALA outperforms the comparison algorithms on benchmark functions and discrete optimization problems, demonstrating its superior optimization performance. The XGBoost surrogate model achieves an R² of 0.8447 and an RMSE of 0.0841 mm, indicating good predictive accuracy. In welding path planning simulations, the proposed method effectively reduces the path length and welding deformation while achieving a favorable trade-off between path efficiency and structural quality. Robotic welding experiments further validate its practical effectiveness. Full article

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

Dendrobium polysaccharide is an important indicator for evaluating the quality of Dendrobium huoshanense. To improve the prediction accuracy of polysaccharide content, this study proposes an improved Artificial Lemming Algorithm (IALA) optimized BP neural network model. In IALA, a periodic mutation strategy and a fast hybrid opposition learning strategy (FHOBL) are introduced to enhance population diversity, improve global search ability, and avoid premature convergence. The proposed IALA was first evaluated on CEC2017 and CEC2020 benchmark functions. Experimental results show that IALA achieves better or competitive performance compared with seven other algorithms in terms of mean fitness, best fitness, and standard deviation. Statistical tests, including Wilcoxon rank-sum and Friedman tests, further verify the significant superiority and robustness of IALA. Then, IALA was used to optimize the initial weights and thresholds of BP neural networks for Dendrobium polysaccharide content prediction. The results show that IALA-BP achieves the best overall prediction performance, with an R² of 0.8731, RMSE of 2.1581, and MSE of 4.6683. Compared with standard BP and other optimized BP models, IALA-BP provides more accurate and stable prediction results. Therefore, the proposed IALA-BP model is effective for rapid prediction of Dendrobium polysaccharide content. Full article

Multi-threshold image segmentation is a common technique in computer vision and image analysis. However, segmentation quality suffers greatly as the number of thresholds increases, particularly for color image segmentation tasks. To address this challenge, this paper proposes a modified reptile search algorithm (MRSA) based on Otsu and Kapur objective functions. Firstly, an RSA algorithm is developed by combining an adaptive weight factor and elite-guided learning to improve segmentation performance. Secondly, an RGB channel symmetric cooperation mechanism is introduced to exchange information among color channels. Thirdly, a repair mechanism is designed to maintain the structural symmetry of solutions throughout the optimization process. We conduct extensive experiments on the BSD500 benchmark color images under different threshold levels and compare MRSA with an improved bald eagle search algorithm (IBES), enhanced Giza pyramids construction algorithm (GGPC), multi-mechanism artificial lemming algorithm (MALA), and RSA. The experimental results demonstrate that the proposed MRSA algorithm achieves superior segmentation performance in terms of objective function values, region covering, peak signal-to-noise ratio, structural similarity index measure, and feature similarity index, and it exhibits excellent results even at high threshold levels. Full article

Multi-objective optimization algorithms are essential for solving complex engineering problems. However, conventional approaches often struggle with limited convergence accuracy and poor solution diversity. This paper proposes the Improved Multi-objective Artificial Lemming Algorithm (IMOALA), incorporating three novel components: an elite selection strategy, a differential-guided external archiving strategy, and a non-uniform mutation strategy. The performance of the IMOALA is benchmarked against other algorithms across twelve test functions and further validated on IEEE 30-bus and 39-bus systems to solve environmental economic dispatch that balances minimal fuel cost and pollutant emission. Results across Inverted Generational Distance (IGD), Maximum Spread (MS), and Generational Distance (GD) metrics demonstrate that the IMOALA achieves superior convergence precision and solution diversity in numerical tests. In engineering applications focusing on fuel cost and emission reduction, the IMOALA consistently yielded higher Normalized Distance (ND) and lower Spacing (SP) values compared to mainstream competitors, delivering evenly distributed Pareto trade-off solutions for cost–emission coordination. These findings verify the feasibility, robustness, and superiority of the IMOALA, offering a highly effective optimization tool for complex, multi-objective power system dispatch and broader engineering challenges. Full article

Efficient coordination between harvesters and grain trucks is essential for reducing operating cost, machinery waiting time, unnecessary travel, and carbon emissions in large-scale harvesting operations. This study develops a multi-objective cooperative scheduling framework for harvesters and grain trucks, with the objectives of minimizing total operating cost and total carbon emissions. The model integrates field operation sequencing, harvester assignment, harvester–truck service matching, machinery capacity constraints, waiting cost, and weather-dependent operational efficiency. To solve the resulting discrete and nonlinear scheduling problem, an artificial lemming algorithm with adaptive behavior selection and neighborhood cooperation (ALA-AN) is proposed. The algorithm uses a three-layer encoding structure to represent field sequences, harvester assignments, and grain truck service relationships and incorporates adaptive search, neighborhood cooperation, fast non-dominated sorting, and crowding-distance-based selection to improve Pareto-front quality. Computational experiments were conducted using real farm data and scheduling instances of different scales. The results show that the ALA-AN achieves better Pareto-front performance than the benchmark algorithms in terms of convergence, diversity, operating cost, and carbon emissions, with more evident advantages in medium- and large-scale scenarios. In the retrospective case-based comparison using Jianbian Farm records, the ALA-AN-based scheduling scheme reduced the estimated total operating cost by 5.85% and estimated total carbon emissions by 3.07% compared with the experience-based scheduling scheme. These results indicate that the proposed framework can support cost-effective and low-carbon decision making for cooperative agricultural machinery scheduling. Full article

The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown considerable promise, they frequently encounter difficulties such as premature convergence, inadequate local refinement, and diminished performance in high-dimensional multimodal environments. To overcome these issues, this study presents HALA, a new hybrid dual-subpopulation optimizer that effectively integrates an enhanced ALA with the SHADE algorithm. HALA employs two interacting subpopulations: one leverages an improved ALA with hybrid t-distribution and Levy flight perturbations to promote persistent long-range exploration and diversity preservation; the other applies SHADE’s success-history adaptation and external archive for accurate local exploitation. Periodic bidirectional elite migration facilitates knowledge transfer between the subpopulations, reducing early stagnation in the enhanced ALA and strengthening SHADE’s global search capability. HALA is thoroughly benchmarked against 17 advanced metaheuristics, including ALA, LSHADE, LSHADE-SPACMA, AOOA, BAEO, BPBO, CCO, CEO, CQALA, DFL, DMOA, DHOA, FGO, KLA, PGA, SO, and SOO, using the IEEE CEC2017 suite in 10, 30, 50, and 100 dimensions and the IEEE CEC2022 suite in 10 dimensions. Comprehensive analyses involving qualitative visualization, convergence curves, boxplots, and statistical tests indicate that HALA achieves competitive or superior solution quality, comparable or faster convergence, and robust stability on a substantial proportion of the test instances. In particular, HALA obtains the most favorable Friedman average ranking values among the compared algorithms, which are 2.55, 2.38, 2.34, and 2.55 for the 10-, 30-, 50-, and 100-dimensional CEC2017 functions, respectively, and 2.58 for the 12 10-dimensional CEC2022 functions. Moreover, HALA is successfully applied to five well-known constrained engineering design problems—pressure vessel, rolling element bearing, tension/compression spring, cantilever beam, and gear train—where it reliably achieves optimal or near-optimal results that match or surpass the compared methods. These findings underscore HALA’s competitive strength and broad potential for practical engineering optimization. Full article

Offshore wind energy offers abundant resources and significant potential for large-scale development. Efficient design of collection systems is critical to the economic viability of offshore wind farms (OWFs). This study proposes an upper–lower level topology optimization framework based on the Artificial Lemming Algorithm (ALA) to address the complexity arising from large numbers of wind turbines (WTs). At the upper level, wind turbines can be partitioned into different numbers of regions according to practical engineering requirements using the Radial Fuzzy C-Means (RFCM) clustering algorithm. At the lower level, the ALA is applied to optimize the collection system topology within each region, aiming to minimize total construction cost while satisfying operational constraints. A case study involving a 75-WT offshore wind farm is conducted. Comparative simulations against various heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) show that the proposed method achieves faster convergence, lower total costs and greater robustness. Specifically, the ALA reduces the best cost by 9.9% and improves average runtime by 28.5%, indicating its advantages in best-cost search and computational efficiency in the tested case. In addition, based on 10 independent runs, the ALA achieves the lowest median cost of $6684\times {10}^4$ CNY, with an interquartile range of 6593–$6813\times {10}^4$ CNY and a cost range of 6362–$7087\times {10}^4$ CNY. Overall, the proposed framework provides a practical optimization approach for obtaining low-cost feasible collection-system layouts in the studied offshore wind farm case. Full article

Aiming at the problem that it is difficult for existing path planning methods to plan UAV paths in real time in complex atmospheric turbulence environments, this work proposes a dynamic path planning method for UAVs based on an improved artificial lemming algorithm. First, using temperature, pressure, and wind vectors from WRF/NWP forecast data, a dynamic turbulence-change environment model in the airspace is constructed. Then, a UAV dynamic path planning model is formulated by comprehensively considering the turbulence change rate and path safety evaluation factors. Next, to address premature convergence of existing algorithms under turbulence influence, a solving method for the UAV dynamic path planning model based on an improved artificial lemming algorithm is developed. Simulation results show that, under the proposed replanning mechanism, the improved algorithm reduces the final fitness by 36.19% and cumulative turbulence exposure by 16.28% on average compared with all competing methods. Full article

Complex engineering optimization problems often exhibit high-dimensional, multi-constraint, and nonlinear characteristics. Traditional deterministic optimization methods rely on gradient information and have limited optimization ranges, making it difficult to meet the requirements of efficient and accurate solutions. Intelligent optimization algorithms have become the core means of solving such problems. Aiming at the limitations of the standard artificial lemming algorithm (ALA), such as insufficient population diversity, premature convergence, weak local exploitation ability, and slow convergence speed, which make it difficult to meet the requirements of solving complex engineering optimization problems, this paper proposes a reverse mutation for optimization learning artificial lemming algorithm (RMALA). Based on the ALA algorithm, the algorithm integrates three strategies: Cauchy mutation, the improved salp swarm algorithm (ISSA), and reverse mutation for optimization learning. The Cauchy mutation is used to maintain population diversity and avoid premature convergence of the algorithm. The improved salp swarm algorithm enhances the local exploitation ability of the algorithm and improves the optimization accuracy. Reverse mutation for optimization learning guides the population toward the global optimal solution region and accelerates the convergence speed. The significant experimental results show that in the CEC2017 and CEC2022 standard test sets, as well as the three classic engineering constrained optimization problems of welded beams, cantilever beams, and pressure vessels, RMALA’s optimization accuracy is improved by more than 30% compared to the original ALA, and its convergence speed is improved by more than 25%. Its stability and robustness are better than those of five new swarm intelligence algorithms proposed in recent years. It can efficiently solve complex high-dimensional, nonlinear constrained optimization problems and has high significant engineering application value and academic innovation. Full article

In the cloud computing environment, various heterogeneous architectures have emerged, and the cloud-edge collaborative task scheduling architecture has come into being under this background. However, the complexity of cloud-edge heterogeneous architecture significantly restricts the improvement of scheduling performance. Therefore, researchers propose solving this problem by leveraging intelligent optimization algorithms. The Artificial Lemming Algorithm has received extensive attention due to its strong robustness. However, when dealing with the problem of cloud-edge collaborative task scheduling, there are still some drawbacks, such as long system response time and unstable scheduling performance. In response to the above problems, this paper proposes a multi-strategy artificial lemming algorithm. Specifically, by coordinating high-order Chebyshev polynomials with chaotic mapping to enhance the richness of the initial population, the scheduling response time is indirectly shortened. Secondly, the Adaptive Spatial Search Mechanism is introduced to make up for the deficiencies in the exploration stage, enhance the algorithm’s exploration ability, and thereby improve the optimization effect of scheduling satisfaction. Furthermore, the Bernstein-Guided Correction Strategy is introduced to enhance the exploitation capability of the algorithm to improve the stability of cloud-edge scheduling. The experimental results demonstrate that compared with the baseline algorithms, the proposed MALA reduces the total scheduling cost by at least 3% across cloud-edge collaborative resource scheduling problems of different scales. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

Unmanned aerial vehicle path planning faces multiple challenges in terms of effectiveness and safety. Traditional optimization methods are difficult to use to effectively find the best route. An enhanced artificial lemming optimization algorithm (ALAEN) is proposed here, which introduces stochastic differential mutation and Beta opposition-based learning into the artificial lemming algorithm (ALA). The comparison with other algorithms on the CEC2017 test set shows that it can effectively improve the optimization ability and convergence speed of the artificial lemming algorithm. Among all algorithms, ALA has an overall ranking of 5.45 and ALAEN has a ranking of 1.34. The ability of ALAEN to solve the actual problem of UAV trajectory planning is tested on two different maps, and it is found that it can effectively improve the path planning ability and ensure safety compared with the ALA. In the small map scene, the average cost function of ALA is 92.999, and the average cost function of ALAEN is 91.598, which is a significant improvement. Compared with other algorithms, ALAEN has the shortest trajectory route and trajectory cost function. Full article

Magnetic ball suspension (MBS) systems are widely used as benchmark platforms in control engineering due to their nonlinear dynamics and inherent open-loop instability, which pose substantial challenges for conventional linear control strategies. The objective of this study is to investigate a hyperbolic tangent–based proportional–integral–derivative (tanh-PID) control structure for MBS systems and to assess the suitability of the artificial lemming algorithm (ALA) for tuning its parameters within a simulation-based benchmark framework. The proposed approach embeds smooth nonlinear signal shaping through the hyperbolic tangent function directly into the classical PID structure, while controller parameters are obtained via metaheuristic optimization using ALA. A performance index balancing overshoot suppression and tracking error minimization is adopted, and the controller is evaluated on a linearized MBS model to ensure comparability with existing studies. Simulation results demonstrate that the optimized tanh-PID controller achieves improved transient and steady-state performance, including a rise time of 0.0144 s, settling time of 0.0275 s, overshoot of 2.98%, and a steady-state error of 2.69 × 10⁻⁵, when compared with classical PID, fractional-order PID (FOPID), and real PID with second-order derivative (RPIDD²) controllers under identical conditions. The results indicate that bounded nonlinear preprocessing combined with metaheuristic-based parameter tuning can provide an effective and practical control alternative for unstable nonlinear systems such as magnetic ball suspension systems. Full article