Convergence Factor Research Articles

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Solving multi-objective green flexible job shop scheduling problem by an improved chimp optimization algorithm

As environmental contamination becomes more and more severe, enterprises need to consider optimizing environmental criteria while optimizing production criteria. In this study, a multi-objective green flexible job shop scheduling problem (MO_GFJSP) is established with two objective functions: the makespan and the carbon emission. To effectively solve the MO_GFJSP, an improved chimp optimization algorithm (IChOA) is designed. The proposed IChOA has four main innovative aspects: 1) the fast non-dominated sorting (FDS) method is introduced to compare the individuals with multiple objectives and strengthen the solution accuracy. 2) a dynamic convergence factor (DCF) is introduced to strengthen the capabilities of exploration and exploitation. 3) the position weight (PW) is used in the individual position updating to enhance the search efficiency. 4) the variable neighborhood search (VNS) is developed to strengthen the capacity to escape the local optimum. By executing abundant experiments using 20 benchmark instances, it was demonstrated that the developed IChOA is efficient to solve the MO_GFJSP and effective for reducing carbon emission in the flexible job shop.

Time-Optimized Robot Trajectory Based on Improved Whale Optimization Algorithm in Intelligent Manufacturing

Abstract To discuss the low convergence accuracy of whale optimization algorithm (WOA) and the problem of converging to local optima, we proposed using nonlinear convergence factors and introducing nonlinear inertia weights in the WOA. The modified WOA was used to optimize the trajectory of a six-degrees-of-freedom (6DOF) industrial robot. To improve the convergence accuracy and the local and global search ability of the WOA, we first replaced the convergence factor with a nonlinear convergence factor and added a nonlinear inertia weight. The algorithm was used along with a quintic polynomial equation to develop a time-optimal trajectory, for the robot, for use in practical application scenarios. Simulation experiment results showed that the duration of a complete loading–unloading process was reduced by 30% after robot motion trajectory optimization compared with that before optimization, indicating the effectiveness of the improved WOA and its suitability for robot trajectory optimization.

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized variational mode decomposition (VMD) and improved deep belief network-extreme learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian–Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of generalized Gaussian distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract GDE. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

Optimized parameterized Uzawa methods for solving complex Helmholtz equations

As we know that, the parameterized Uzawa (PU) method can be very efficient when used to solve the standard saddle point problem, especially, when we have good and accurate estimation of preconditioned Schur complement matrix. In this paper, by taking full use of the special structure of coefficient matrix arising from complex Helmholtz equations, two types of optimized PU (OPU) methods are discussed theoretically and experimentally. Specifically, the convergence factors of these two OPU methods are less than 0.172, and the optimal result of first OPU method can reach 0.0396 with σ1≥σ2>0, which is currently the best theoretical result in the literatures. Moreover, the second OPU method has better computational advantages compared with the first OPU method as it avoids the inverse computation of W at each step, indicating the less CPU time will be costed by the second OPU method. In addition, the optimal parameters involved in our algorithms consist of constants, reducing the computational complexity associated with parameter selection. Finally, numerical results are given, not only show the effectiveness of OPU methods, but also confirm the rationality of theoretical analysis.

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

基于改进鲸鱼优化算法的多农业搬运机器人协同作业轨迹规划研究

To solve the problem that the moving trajectory and operating trajectory are relatively independent and time-consuming when robots transfer agricultural products from harvesting fields to warehouses or transport vehicles, a type of agricultural materials handling robot was designed, the optimal trajectory planning method for the collaborative operating time of agricultural materials handling robots was proposed, and the time optimal trajectory under the collaborative operation of robot operating system and traveling system was acquired. Specifically, the kinematic model and dynamic model for the collaborative operation of robots were established to perform time optimal trajectory planning for materials handling robots, the Beta distribution was then applied to the Whale Optimization Algorithm (WOA) for population initialization, and a nonlinear convergence factor was introduced to prevent local optimum in the later stage of iterations. Finally, WOA was improved combining the variable neighborhood algorithm to enhance the diversity of the neighborhood structure, and this improved algorithm was applied to model solving. The results reveal that the proposed trajectory planning method can facilitate robots to obtain a smooth and time optical moving trajectory in collaborative operations of materials grabbing and discharging and obstacle avoidance. The displacement, speed, acceleration, and force/torque curves of each joint of the robots change gently, and the double-crawler traction can meet the requirements of the robots and rapidly stabilize and track the time optical trajectory.

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

Selective harmonic elimination in PUC‐5 multilevel inverter using hybrid IGWO‐DE algorithm

AbstractThe use of a multilevel inverter has received significant attention in recent years due to its numerous advantages. Because of the widespread use of multilevel inverters in industries and applications that require a wide range of voltages, achieving high‐quality voltage has presented a number of challenges. Many studies have been carried out to address the problem of unwanted harmonics in multilevel inverters. The inverter switching can be done at a low frequency using the Selective Harmonic Elimination (SHE) technique, and the unwanted harmonics can be significantly reduced; however, the issue with SHE is solving the transcendental equations and determining the optimum switching angle. To address this problem, a new and improved hybrid algorithm is proposed that combines two evolutionary algorithms, gray wolf optimization (GWO) with an improved and new convergence factor and Differential evolution (DE) with a dynamic scaling factor using a crossover operator. In this paper, the optimum switching angles for a packed U cell 5‐level inverter are estimated using the proposed algorithm for distinct modulation index values, and simulation results are compared with different algorithms. The simulation result of the proposed algorithm, IGWO‐DE is confirmed through experiment.

IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.

MANAGEMENT MODEL FOR HIGHER EDUCATION INSTITUTIONS BASED ON RANKING OF UNIVERSITIES

The demands and pressures of the market are felt in higher education, necessitating continuous improvement in every aspect. A desirable approach would be implementing a management model of excellence in education, particularly emphasizing evaluation, and service enhancements. Such assessments can influence the positioning of universities in the market and enable them to identify their strengths and weaknesses. This study proposes a conceptual model of excellence management for HEIs (Higher Education Institutions), based on analysing university rankings. The model will be developed by identifying convergent factors of the EFQM and BCPE Management Models, verifying convergence factors among the leading five international rankings, and presenting the procedures for using Rankings as Performance Indicators. This research has a bibliographic character. As a result of this research, it was possible to propose a management model of excellence in which university rankings are used as performance indicators. The proposed model is flexible and can be utilized by different HEIs.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/lit/0757/a.php" alt="Hit counter" /></p>

A chimp algorithm based on the foraging strategy of manta rays and its application.

To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. Introducing the Latin hypercube method to construct the initial population so that the individuals of the initial population are evenly distributed in the solution space, increasing the diversity of the initial population. Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. The manta ray foraging strategy is introduced at the position update to make up for the defect that the algorithm is prone to local optimization, which effectively improves the optimization performance of the algorithm. To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. Finally, in order to further verify the algorithm's applicability in actual production processes, it was applied to solve scheduling problems in three flexible workshop scenarios and an aviation engine job shop scheduling in an enterprise. This confirmed its efficacy in addressing complex real-world problems.

A Large-Scale Multiobjective Particle Swarm Optimizer With Enhanced Balance of Convergence and Diversity.

Large-scale multiobjective optimization problems (LSMOPs) continue to be challenging for existing multiobjective evolutionary algorithms (MOEAs). The main difficulties are that: 1) the diversity preservation in both the objective space and the decision space needs to be taken into account when solving LSMOPs and 2) the existing learning structures in current MOEAs usually make the learning operators only coincidentally serve convergence and diversity, leading to difficulties in balancing these two factors. Therefore, balancing convergence and diversity in current MOEAs is difficult. To address these issues, this article proposes a multiobjective particle swarm optimizer with enhanced balance of convergence and diversity (MPSO-EBCD). In MPSO-EBCD, a novel velocity update structure for multiobjective particle swarm optimization is put forward, dividing the convergence, and diversity preservation operations into independent components. Following the proposed update structure, a weighted convergence factor is introduced to serve the convergence strategy, whilst a diversity preservation strategy is built to uniformly distribute the particles in the searched space based on a proposed multidimensional local sparseness degree indicator. By this means, MPSO-EBCD is able to balance convergence and diversity with specific parameters in independent operators. Experimental results on LSMOP benchmarks and a voltage transformer optimization problem demonstrate the competitiveness of the proposed algorithm compared to several state-of-the-art MOEAs.

Multiple strategies based Grey Wolf Optimizer for feature selection in performance evaluation of open-ended funds

The methods for selecting the features in evaluating fund performance rely heavily on traditional statistics, which can potentially lead to excessive data dimensions in a multi-dimensional context. Grey Wolf Optimizer (GWO), a swarm intelligence optimization algorithm with its simple structure and few parameters, is widely used in feature selection. However, the algorithm suffers from local optimality and the imbalance in exploration and exploitation. This paper proposes a Multi-Strategy Grey Wolf Optimizer (MSGWO) to address the limitations, and identify the relevant features for evaluating fund performance. Random Opposition-based Learning is applied to enhance population quality during the initialization phase. Moreover, the convergence factor is nonlinearized to coordinate the global exploration and local exploitation capabilities. Finally, a two-stage hybrid mutation operator is applied to modify the updating mechanism, so as to increase population diversity and balance the exploration and exploitation abilities of GWO. The proposed algorithm is compared against 6 related algorithms and verified by the Wilcoxon signed-rank test on 12 quarterly datasets (2020-2022) of Chinese open-ended funds. The results inform that MSGWO reduces the feature size as well as the classification error rate.

A grey wolf optimization algorithm for solving partial destructive disassembly line balancing problem consider feasibility evaluation and noise pollution

Partial destructive disassembly (PDD) is a crucial tool for enhancing efficiency and reducing disassembly costs when recycling complex end-of-life (EOL) products. Evaluating the feasibility of PDD is crucial for determining its implementation. However, the existing partial destructive disassembly line balancing (PDDLB) problem prioritizes profitability and randomly selects components for destructive, disregarding the feasibility of PDD and the noise pollution resulting from destructive operations. To address this concern, this paper introduces the integration of feasibility evaluation and noise considerations into the PDDLB problem for the first time. Subsequently, a mathematical model is established for the PDDLB problem, aiming to minimize disassembly costs, achieve smoothness balance, minimize the number of stations, and mitigate the adverse effects of noise. Then, an improved grey wolf optimization algorithm (IGWOA) considering feasibility evaluation and noise limitations is developed. It non-linearizes the convergence factor to enhance accuracy in the search for optimality, introduces a Lévy flight operation to further expand the search solution space, and incorporates an interference factor to prevent local optimality. In addition, the superiority of the proposed algorithm in solving different cases is verified by comparing five algorithms for the 25-task example and seven algorithms for the 52-task example in the latest literature. Finally, the proposed method is applied to a real engine case. The obtained results are compared with several classical Swarm Intelligence methods. The comparison results show that IGWOA has advantages due to other group intelligence methods in solving small, medium, and large-scale example. Moreover, the disassembly cost is comparatively low.

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

Designing and Application of Modified SCSO-Based LADRC Controller for Dicing Saw Chuck Table Systems

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results.

Collocated control of double electric pushrod in a harvester header based on an improved gray wolf PID algorithm

In order to further enhance energy conservation and emission reduction, the header lifting structure of a harvester is studied. First, a double electric pushrod structure is used to replace the oil cylinder and air cylinder lifting structure of a traditional header to reduce fuel consumption and harmful gas emission. Furthermore, a mathematical model and a simulation model of the electric pushrod are established. To enhance the control effect of the header lifting structure, an improved version of the traditional gray wolf optimization (GWO) algorithm is designed. The nonlinear convergence factor, Kent chaotic mapping and convergence surrounding and spiral updating operations are introduced to increase the convergence speed and optimization accuracy of this algorithm. The improved GWO (IGWO) algorithm is applied to optimize the proportional-integral-derivative (PID) controller of the double pushrod coordinated control system. Then, a new IGWO-PID control algorithm is also designed. The cross-coupling control strategy of header’s double pushrods is then studied. Results of the simulation and bench test show that the IGWO-PID control algorithm and the cross-coupling control strategy can effectively enhance controlling effect of the harvester header. The left and right pushrods can achieve good synchronous and coordinated movements.

Research on Solving Flexible Job Shop Scheduling Problem Based on Improved GWO Algorithm SS-GWO

As an important branch of production scheduling, the flexible job shop scheduling problem (FJSP) is a typical NP-hard problem. Researchers have adopted many intelligent algorithms to solve the FJSP problem, nonetheless, the task of dynamically adapting its essential parameters during the computational process is a significant challenge, resulting in the solution efficiency and quality failing to meet the production requirements. To this end, this paper proposes an adaptive gray wolf fast optimization algorithm (SS-GWO), which adopts the gray wolf algorithm (GWO) as the basic optimization method, and the algorithm adaptively selects the global search or local search according to the degree of agglomeration of individuals. Firstly, a non-linear convergence factor strategy is employed to control the global exploration and local exploitation capabilities of the algorithm at different stages. This enhances optimization precision and accelerates convergence speed, achieving a dynamic balance between the two. Secondly, the spiral search mechanism of Whale Optimization Algorithm is used in GWO to improve the exploration capability of Gray Wolf Optimization Algorithm. Finally, the effectiveness of SS-GWO model is verified by comparison experiments. The comparison demonstrates the superiority of SS-GWO over the other five state-of-the-art algorithms in solving the 22 classical benchmark test functions. SS-GWO is applied to solve FJSP by means of the standard test function bandimarte calculus. The optimal solution and performance of SS-GWO for solving FJSP are compared with other algorithms. The experimental results show that the SS-GWO algorithm has good optimization performance, and the maximum completion time is reduced by 19% and 37% compared with that of IGWO and GWO, respectively, and the proposed SS-GWO algorithm achieves a better solution effect on flexible job shop scheduling instances, which can satisfy the actual production scheduling needs.