Local Ability Research Articles

ESD-YOLOv5: A Full-Surface Defect Detection Network for Bearing Collars

To address the different forms and sizes of bearing collar surface defects, uneven distribution of defect positions, and complex backgrounds, we propose ESD-YOLOv5, an improved algorithm for bearing collar full-surface defect detection. First, a hybrid attention module, ECCA, was constructed by combining an efficient channel attention (ECA) mechanism and a coordinate attention (CA) mechanism, which was introduced into the YOLOv5 backbone network to enhance the localization ability of object features by the network. Second, the original neck was replaced by the constructed Slim-neck, which reduces the model’s parameters and computational complexity without sacrificing accuracy for object detection. Furthermore, the original head was replaced by the decoupled head from YOLOX, which separates the classification and regression tasks for object detection. Last, we constructed a dataset of defective bearing collars using images collected from industrial sites and conducted extensive experiments. The results demonstrate that our proposed ESD-YOLOv5 detection model achieved an mAP of 98.6% on our self-built dataset, which is a 2.3% improvement over the YOLOv5 base model. Moreover, it outperformed mainstream one-stage object detection algorithms. Additionally, the bearing collar surface defect detection system developed based on our proposed method has been successfully applied in the industrial domain for bearing collar inspection.

UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios.

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model's attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

Simultaneous estimation of thermal and optical properties of molten salt based on improved colliding bodies optimization

Inverse estimation of thermal and optical properties of molten salt is studied in this study. An improved colliding bodies optimization (ICBO) algorithm is developed to solve inverse problems, in which three improvements is proposed: (1) chaos theory is introduced to optimize initial positions of colliding bodies; (2) a self-memory strategy is proposed to improve local search ability; and (3) a random search strategy is proposed to improve global search ability. And the test functions prove that the ICBO algorithm is more efficient and robust than the original colliding bodies optimization algorithm. A two-step retrieval strategy is proposed to overcome serious crosstalk problem among different parameters, in which optical parameters are inversed based on the transmittance-reflectance model firstly, and then thermal parameters are inversed based on the transient coupled conduction-radiation model and known optical parameters obtained in the first step. All research outcomes indicate that the constant and temperature-dependent thermal and optical parameters of molten salt can be estimated accurately by the ICBO algorithm and two-step retrieval strategy. Even if considering 10% of measurement error, the maximum relative error is only 6.50% (estimation of thermal conductivity). Therefore, this work is beneficial for improving utilization of molten salt for harvesting solar energy.

Lithium battery model parameter identification based on the GA-LM algorithm

ABSTRACT The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy.

Feature selection based on a multi-strategy African vulture optimization algorithm and its application in essay scoring

Reducing the dimensions of the original data set while preserving the information as much as possible is conducive to improving the accuracy and efficiency of the model. To achieve this, this paper presents a multi-strategy African vulture optimization algorithm that is the chaotic and elite opposition-based African vulture optimization with the simplex method and differential evolution strategy(CESDAVO). Three main improvements are introduced into African vultures optimization(AVO) to improve its capabilities in this study. Firstly, the chaotic elite opposition-based learning strategy is used to initialize and diversify individual positions of vultures. Secondly, the simplex method is used to optimize those poor individuals so as to further improve the local exploitation ability of the algorithm. Thirdly, the differential evolution strategy is used to make the algorithm escape from the local optimum and improve the global optimization capability of the algorithm. The results of the ablation experiments show that mixing the three strategies greatly improves the optimization performance of the algorithm. In addition, Nine algorithms are compared with CESDAVO on 15 benchmark functions, and this experimental result shows that its optimization capability is superior to the others. Then, the proposed CESDAVO is employed for feature selection, and 12 standard datasets are used for experiments. According to the experimental results, CESDAVO obtained the highest average classification accuracy on 11 datasets and the highest feature selection rate on 8 datasets, which is significantly better than other algorithms. Finally, CESDAVO is also applied to feature reduction for essays, removing 24 features and significantly improving the classification accuracy on multiple classifiers.

The spatial distribution of insect communities of a mountaintop forest archipelago is not correlated with landscape structure: A multitaxa approach

Abstract Understanding community assembly in habitat patches requires an integrative approach, considering the potential role of local and regional factors and organisms' dispersal abilities among patches. For this, assessing the degree of species overlap between patches (nestedness) can be particularly informative, especially regarding different taxa and distinct patch properties. We assessed the potential role of landscape‐patch structure in shaping the nestedness patterns of five taxonomic groups with distinct dispersal abilities (ants, bees, dung beetles, fruit‐feeding butterflies and aculeate wasps). We collected insects from 11 forest patches in the Espinhaço mountain range in southeast Brazil. We assessed the potential contribution of three forest island attributes (size, distance and shape) to the nestedness of insect communities and then tested if metacommunities were significantly nested with respect to habitat (forest islands). Finally, we evaluated how species of each taxonomic group contribute to metacommunity nestedness. While there was a substantial variation in the contribution to the nested pattern within each taxon, all taxa studied exhibited a significantly nested pattern of species distribution. The landscape‐patch structure and the vagility of the different taxa did not influence the contribution of sites to the metacommunity nested pattern. Our integrative approach provides critical data on the role of different factors in shaping species distribution in natural patches. Species with different dispersal abilities and patches with distinct landscape properties contributed similarly to nestedness, indicating the potential role of the vegetation matrix permeability in shaping the distribution of organisms in the studied forest archipelago.

Quantum and Condor-Based Brainstorming Optimization Algorithm for NOx Prediction

This paper proposed a quantum bald eagle brainstorm (QBBSO) based on quantum initialization and combined with the bald eagle optimization algorithm in light of the original Brain Storm Optimization (BSO) algorithm’s strong local search ability, which would result in local optimization, a poor optimization effect, and difficult development. To accomplish the randomness of the number and increase the randomness of the population, we first modified the initialization method of the original brainstorming population, introduced the idea of quantum code, and then translated the binary numbers 0 and 1 to the decimal number. To achieve the best outcome, the original step size formula was employed for global selection, local search, and final selection using the vulture search method. The original BSO was then optimized to attract more global individuals. The algorithm versions were compared using the common benchmark function test. The findings demonstrated that QBBSO had a greater capacity for global search and a faster convergence speed. This research also applied the QBBSO algorithm to the long-term and short-term memory network (LSTM) to forecast the NOx concentration in the boiler, further demonstrating the algorithm’s superiority in real-world settings.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses. However, the traditional kernel fuzzy C-means (KFCM) algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions. To overcome these limitations, an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper. This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm. The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio. Compared with the traditional KFCM algorithm, the enhanced KFCM algorithm has robust clustering and comprehensive abilities, enabling the efficient convergence to the global optimal solution

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation.

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with six CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors.

A Pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility

The previous studies on the flexible job shop scheduling problems (FJSP) with machine flexibility and worker flexibility normally assume that each machine is operated by one worker at any time. However, it is not accurate in many cases because many workers may be required for machines in processing complex operations. Hence, this paper studies a universal version, i.e., FJSP with worker cooperation flexibility (FJSPWC), which defines that each machine can be used only if their required workers are prepared. A mixed-integer linear programming model tuned by CPLEX is established for the problem aiming to collaboratively minimize the makespan, maximum workload of machines and maximum workload of workers. To solve the problem efficiently, a Pareto-based two-stage evolutionary algorithm (PTEA) is proposed. In the PTEA, a well-tailored initialization operator and the NSGA-II structure are designed for global exploration in the first stage, and a competitive objective-based local search operator is developed to improve its local search ability and accelerate the convergence in the second stage. Extensive experiments based on fifty-eight newly formulated benchmarks are carried out to validate the effectiveness of the well-designed initialization operator and two-stage architecture. Comprehensive experiments are performed to evaluate the proposed PTEA, and the results reveal that the PTEA is superior to four comparison algorithms concerning the distribution, convergence, and overall performance.

Utility of Carbon Dioxide Venography and Intraprocedural CT for Adrenal Venous Sampling in Patients with an Allergy to Iodinated Contrast Media

PurposeTo assess the diagnostic performance of carbon dioxide (CO2) and intraprocedural unenhanced computed tomography (CT) for adrenal venous sampling (AVS) (CO2–intraprocedural unenhanced CT–AVS) in patients with primary aldosteronism (PA) and a history of iodine contrast medium allergy. Materials and MethodsCO2–intraprocedural unenhanced CT–AVS was performed in 18 patients with iodine contrast media allergies at the authors’ hospital between December 2015 and January 2021. CT and noncontrast magnetic resonance angiography were used to evaluate the preoperative adrenal vein anatomy. CO2 venography was performed to confirm adrenal vein catheterization. Additionally, intraprocedural unenhanced CT was also performed to confirm catheter position in the right adrenal gland. ResultsIn all cases in which CO2–intraprocedural unenhanced CT–AVS was performed, the right and left adrenal veins were catheterized appropriately, leading to a localized diagnosis. Catheterization of the left adrenal vein was confirmed using CO2 venography in all cases. In 7 of the 18 cases, CO2 venography demonstrated selection of the right adrenal vein. In 15 of 18 cases, intraprocedural unenhanced CT demonstrated selection of the right adrenal vein. ConclusionsCO2–intraprocedural unenhanced CT–AVS demonstrated the same diagnostic ability for PA localization as conventional AVS with iodine contrast media. The proposed method is clinically feasible for AVS, in which iodine contrast media use is restricted.

Identification of Mechanical Parameters in Flexible Drive Systems Using Hybrid Particle Swarm Optimization Based on the Quasi-Newton Method

This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a hybrid optimization method for accurately identifying mechanical parameters in two-mass model (2MM) systems. These systems are commonly used to model and control high-performance electric drive systems with elastic joints, which are prevalent in modern industrial production. The proposed method combines the global exploration capabilities of particle swarm optimization (PSO) with the local exploitation abilities of the quasi-Newton (QN) method to precisely estimate the motor and load inertias, shaft stiffness, and friction coefficients of the 2MM system. By integrating these two optimization techniques, the HPSO-QN method exhibits superior accuracy and performance compared to standard PSO algorithms. Experimental validation using a 2MM system demonstrates the effectiveness of the proposed method in accurately identifying and improving the mechanical parameters of these complex systems. The HPSO-QN method offers significant implications for enhancing the modeling, performance, and stability of 2MM systems and can be extended to other systems with flexible shafts and couplings. This study contributes to the development of accurate and effective parameter identification methods for complex systems, emphasizing the crucial role of precise parameter estimation in achieving optimal control performance and stability.

Application of BPNN Optimized by Fireworks Algorithm on Structural Bearing Capacity Analysis of Transmission Tower under Icing Conditions

With the increasing power demand in city development, the construction and application of transmission towers need to meet higher requirements. Icing is an extreme meteorological condition in the world, and icing disasters cause various accidents such as tower collapse and line disconnection of large-area lines every year. Therefore, it is of great significance to study the ultimate bearing capacity of transmission towers under icing conditions. The structural bearing capacity under icing is greatly affected by meteorological conditions and environmental factors, which has strong randomness and complexity, and brings a series of urgent problems to the structural stability of transmission towers. In the paper, the ultimate bearing capacity analysis and failure path research are conducted on the 500 kV linear cathead tower structure, and a structural bearing capacity prediction model based on the fireworks algorithm (FWA) is established. In order to accurately predict the structural bearing capacity, reduce the adverse effects of line icing, and ensure the structural stability of the transmission tower, the FWA is introduced into the neural network model. The self-adjusting mechanism of the local search ability and global search ability of the FWA is used to optimize the optimization process of weights and thresholds in the neural network, and a structural bearing capacity prediction model based on the FWA improved backpropagation neural network (BPNN) is proposed. Through the analysis and calculation of the measured data of the transmission tower in a certain area, and the comparative study with the traditional BPNN and the BPNN optimized by other common algorithms, the analysis and calculation results of the FWA-BPNN model used in this paper are closer to the actual value, the multiple indicators are more superior, and the model is more stable, which can realize the rapid and accurate analysis of the axial stress of tower members and maximum displacement between member nodes of the transmission tower under the icing condition.

A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers

At present, the energy consumption of the parallel chillers system accounts for about 60% of the whole energy consumption of central air conditioning system and 25– 40% of the total energy consumption. Unreasonable load distribution brings huge energy consumption and carbon emissions during building operation. In order to reduce energy consumption and carbon emissions of the parallel chillers system in large-scale refrigeration system, we proposed an improved sparrow search algorithm (TMSSA). This algorithm initializes the population using Tent chaotic map to increase population diversity, which can enhance the possibility of TMSSA to obtain the optimal solution before the optimization iteration, and speed up the convergence process. Besides, a Levy flight mechanism is applied to improve the position update of the producer, enhancing the randomness and local search ability of this algorithm. Moreover, Gaussian mutation method is utilized to perturb the position of scroungers, strengthening the ability of the algorithm to escape local optima to improve robustness of TMSSA. To evaluate the proposed algorithm, 12 benchmark functions were chosen, and the results showed that it overcomes the limitations of traditional SSA regarding local optima trapping. The algorithm is also used to solve the optimal chiller loading (OCL) problem, which has 3 typical cases, and comparisons were made with other algorithms. The results further demonstrate that TMSSA is highly accurate, fast-converging, and robust with excellent energy-saving performance.

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

With the development of the e-commerce industry, using UAVs (unmanned aerial vehicles) to deliver goods has become more popular in transportation systems. This delivery method can reduce labor costs and improve the distribution efficiency, and UAVs can reach places that are difficult for humans to reach. Because some goods are perishable, the quality of the delivery will have an impact on the customer satisfaction. At the same time, the delivery time should also meet the needs of customers as much as possible. Therefore, this paper takes the distribution distance and customer satisfaction as the objective functions, establishes a bi-objective dynamic programming model, and proposes an improved SPEA2 (strength Pareto evolutionary algorithm 2). The improved algorithm introduces the local search strategy, on the basis of the original algorithm. It conducts a local search for the better non-dominated solutions obtained in each iteration. The new dominated solutions and non-dominated solutions are determined, and the crossover operator is improved, so that the local search ability is improved, on the basis of ensuring its global search ability. The numerical experiment results show that the improved algorithm achieves an excellent performance in three aspects: the Pareto front, generation distance, and spacing, and would have a high application value in UAV cargo delivery and other MOPs (multi-objective optimization problems). The average spacing value of the improved algorithm is more than 20% smaller than SPEA2 + SDE (strength Pareto evolution algorithm 2–shift-based density estimation), which is the second-best algorithm. In the comparison of the average generation distance value, this number reaches 30%.

Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop

In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), the green automated guided vehicle (AGV) routing problem, which involves simultaneous pickup and delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, we conduct a comprehensive analysis of the mechanical forces present during AGV transportation and evaluate the overall operational efficiency of the workshop. Then, we construct a green vehicle path planning model to minimize the energy consumption during AGV transportation and the standby period. Hence, the problems considered in this study are modeled based on asymmetry, making the problem solving more complex. We also design a hybrid differential evolution algorithm based on large neighborhood search (DE-LNS) to increase the local search ability of the algorithm. To enhance the optimal quality of solutions, we design an adaptive scaling factor and use the squirrel migration operator to optimize the population. Last, extensive computational experiments, which are generated from the VRPSPDTW instances set and a real case of an intelligent workshop, are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results show that DE-LNS yields competitive results, compared to advanced heuristic algorithms. The effectiveness and applicability of the proposed algorithm are verified. Additionally, the proposed model demonstrates significant energy-saving potential in workshop logistics. It can optimize energy consumption by 15.3% compared with the traditional VRPSPDTW model. Consequently, the model proposed in this research carries substantial implications for minimizing total energy consumption costs and exhibits promising prospects for real-world application in intelligent workshops.

Formal local government coordination to mitigate climate change

The effectiveness of municipal climate change mitigation policies depends partly on local governments' ability to coordinate with each other regionally. Yet coordination on climate policies beyond network participation and information sharing is rare. This study investigates how local governments can work together on climate policies, focusing on formal horizontal policy coordination. The study examines multi-government coordination to enact Community Choice Aggregation (CCA) in California, an impactful climate policy that lets local governments control energy procurement. The study surveys all multi-government CCA programs in the state, evaluating their characteristics and governance structures. Three regional areas are examined in-depth through policymaker and stakeholder interviews, media coverage, and decision-making outcomes. The study identifies several strategies for success and finds that formal horizontal policy coordination on climate mitigation can provide a path forward in multilevel governance systems for local governments to enact ambitious climate policies together.

EFP-YOLO: A quantitative detection algorithm for marine benthic organisms

Underwater object detection technologies are widely applied in the field of marine scientific research. In real underwater environments, a large number of dense, blurred, and small-sized objects appear. It is difficult for current general-purpose object detectors to detect these samples. To solve these problems, this paper proposes a quantitative detection algorithm EFP-YOLO (Enhance the Ability of Feature Extraction and Perception Ability of YOLOX) for marine benthic organisms that improved on YOLOX. Firstly, IGACSP (Cross Stage Partial Module with Interactive Global Attention) is embedded in the backbone to enhance the ability for extracting features of small-sized and blurred objects, which fuses local and global information in a parallel interactive way. To improve the capacity for detecting multiscale objects, EPPM (Efficient Pyramid Pooling Module) fuses feature maps from different scales that provide sufficient contextual information. Secondly, FSM (Feature Selection Module) and FAM (Feature Alignment Module) are introduced into the feature fusion part for spatial feature extraction and feature alignment, respectively. The modules provide accurate boundary localization information for the detection of dense objects. Finally, ATFhead (Asymmetric Task Focused head) is proposed to increase the scale perception, spatial perception, and task perception of the detection head. It improves the classification and localization abilities of the model, thus achieving accurate counting of marine benthic organisms. The experimental results show that compared with YOLOX-S, mAP0.5:0.95 is improved from 59.7% to 64.6% on the DUO dataset. EFP-YOLO demonstrates superior performance in the quantitative detection of marine benthos compared to other object detection algorithms. This can effectively advance marine scientific research and promote biological conservation efforts. To show the generality of EFP-YOLO, we use a generic object detection dataset for experiments. You can access the code for EFP-YOLO publicly on https://github.com/llllllvv/EFP-YOLO.

TSD-Depth: Using transformers and self-distilling for self-supervised indoor depth estimation

Supervised monocular depth estimation has always been one of the most important tasks in computer vision. With the convolution module as a basic operator, the U-shaped network architecture has become the de facto standard and has achieved tremendous success. However, due to the limited receptive field of the convolution operation, the CNNs are generally inferior in explicitly modeling the long-range dependencies. Originally proposed for natural language processing, the transformers are designed for performing sequence-to-sequence predictions based on global self-attention mechanism. Therefore, the transformers can capture long-range dependencies. However, they have limited localization abilities due to insufficient low-level details. In this work, we propose a TSD-Depth model, which merits both the transformers and the CNNs, as a strong alternative for self-supervised monocular depth estimation. The proposed model simultaneously extracts the global contextual information and local spatial detail features. Furthermore, by designing the hybrid encoder connection method and proper-sized transformer module, the global and local information can more effectively interact. In addition, a local multi-scale fusion block is first proposed to refine the fine-grained details. More importantly, the knowledge is learned by using self-distillation to skip the multi-scale fusion block concatenated with the encoder at the inference time, computed only during the training process for minimal overhead. The experimental results on NYU-v2 and ScanNet datasets show that the proposed TSD-Depth achieves the best performance as compared to the previous state-of-the-art methods.

Developments on metaheuristic-based optimization for numerical and engineering optimization problems: Analysis, design, validation, and applications

Optimization problems are prevalent in a variety of real-world applications, including medical, engineering, chemical, and others, and must be precisely solved to enhance the performance of these applications. Unfortunately, finding near-optimal solutions to these problems is regarded as a hard challenge due to their various characteristics. In a new attempt to solve these problems, this paper presents a new variant of the artificial gorilla troops optimizer (GTO) called ranking-based GTO (RGTO). This variant uses two strategies known as the ranking-based update strategy and the convergence acceleration strategy to improve both the classical GTO's exploitation and exploration capabilities. The first strategy is proposed to enhance each gorilla's local and global search abilities, whereas the latter is intended to enhance GTO's global search abilities to reach better solutions as quickly as possible. First, a recent and challenging benchmark, namely CEC-2017, is utilized to assess the RGTO's explorative and exploitative capabilities. After that, RGTO is used to solve three engineering optimization problems, including parameter estimation problems for both photovoltaic (PV) models and proton exchange membrane fuel cells (PEMFCs), as well as some engineering design problems, to demonstrate how well it performs for real-world optimization problems. Compared to several rival optimizers, the proposed algorithm provides outstanding outcomes for the three engineering optimization benchmark problems considered.