Dynamic Particle Swarm Optimization Algorithm Research Articles (Page 1)

Aiming at the problem that existing equipment importance evaluation methods fail to consider interconnectivity between pieces of equipment, variability after maintenance, and the impact of dynamically changing situations on importance, and focusing on the dynamic support needs of equipment in a conflict environment, this paper proposes a batch allocation method for equipment maintenance tasks considering dynamic importance. The purpose of this study is to determine the batch priority of equipment maintenance based on the dynamically changing importance of pieces of equipment. First, a dynamic importance index system is constructed: a real-time CRITIC-AHP combined weighting method is used to calculate team importance, a dynamic Bayesian network (DBN)-influenced method is used to calculate relative importance, an attention–LSTM time-series prediction method is used to calculate future importance, and then a dynamic entropy weight method is adopted to objectively integrate the three types of importance. Second, a dual-objective optimization model with the maximum equipment importance and the minimum total maintenance time is built, with mobile distance, maintenance time, and maintenance capacity as constraints. The Dynamic Particle Swarm Optimization (DPSO) algorithm is used to solve this model, and its dynamic adaptability is improved through environmental change detection and adaptive adjustment of inertia weight. Finally, the batch allocation of maintenance tasks is realized. Example verification shows that compared with the expert scoring method, the errors of the three importance calculation methods are all reduced by more than 60%, the optimization speed of the dynamic PSO algorithm is 47% faster than that of the static algorithm, and the constructed model has good stability. This method can provide a reference for maintenance support command decisions.

Abstract Systems based on mobile multirobots have gained considerable attention in the past two decades because of their efficacy and flexibility in various real-world applications. An essential component of these systems is multi-robot task allocation (MRTA), which concerns allocating tasks to mobile robots in an efficient manner. The effectiveness of MRTA is influenced by the size of the search space and computational time, and both increase substantially as the number of tasks and robots involved increases. This study introduces an effective solution to the MRTA problem by employing a two-stage approach. First, nearby tasks are automatically grouped into clusters by using an enhanced dynamic distributed particle swarm optimization algorithm. Second, mobile robots are assigned to the closest clusters. To demonstrate the effectiveness of this approach. Simulations are conducted to compare the proposed method with particle swarm optimization and differential evolution approaches. Numerical results confirm that the proposed approach exhibits highly competitive performance in terms of clustering cost, clustering time, and overall time (clustering and assigning time). This approach is advantageous for real-world applications involving numerous robots and targets.

Locating all roots of nonlinear equation of systems (NESs) in a single computational procedure remains a fundamental challenge in computational mathematics. The Dynamic Neighborhood Particle Swarm Optimization algorithm based on Euclidean Distance (EDPSO) is proposed to address this issue. First, a dynamic neighborhood strategy based on Euclidean distance is proposed, to facilitate particles within the population with forming appropriate neighborhoods. Secondly, the Levy flight strategy is integrated into the particle velocity-update mechanism to balance the global search capability and local search capability of particles. Furthermore, integrating a discrete crossover strategy into the PSO algorithm enhances its capability in solving high-dimensional nonlinear equations. Finally, to validate the effectiveness and feasibility of the proposed algorithms, the EDPSO algorithm, along with its comparative counterparts, is applied to solve 20 NESs problems and the forward kinematics equations of a 3-RPS parallel mechanism. Experimental results demonstrate that for the 20 NESs, the EDPSO algorithm achieved the highest success rate (SR = 0.992) and root rate (RR = 0.999) among all compared methods, followed by LSTP, NSDE, KSDE, NCDE, HNDE, and DR-JADE. In solving the forward kinematics of the 3-RPS parallel mechanism, the EDPSO algorithm achieved the highest SR of = 0.9975 and RR = 0.9800, followed by LSTP, KSDE, DR-JADE, NCDE, NSDE, and HNDE, based on these metrics.

A Dynamic State Cluster-Based Particle Swarm Optimization Algorithm

This research aims to use the Particle Swarm Optimization (PSO) algorithm to solve multi-resource and multi-objective problems in enterprise resource scheduling and to improve resource utilization efficiency and overall competitiveness through an adaptive scheduling algorithm. This algorithm intelligently configures and efficiently utilizes resources in complex business environments by dynamically adjusting the parameters and fitness functions of the particle swarm. Research has found that, while maintaining computational cost control, reasonable parameter settings can effectively improve the resource scheduling outcome — especially when the inertia weight is small and the learning factor is large, the performance is optimal. However, increasing the number of particles and learning factors, while enhancing resource utilization, also leads to higher costs. Therefore, in practical applications, the optimal combination of parameters should be selected based on specific conditions. In addition, a comparison between the Dynamic Learning Particle Swarm Optimization (DLPSO) algorithm and standard PSO shows performance differences across various projects, indicating that selecting the appropriate algorithm based on the project context is essential for achieving the best results.

A Dynamic All-Dimensional Adaptive Particle Swarm Optimization Algorithm Prediction Method for the Height of the Water-Conducting Fracture in Complex Stress Environments

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R2 index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R2 index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R2 index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions.

To address the issues of insufficient control parameter identification accuracy and convergence speed during the grid connection of distributed power sources, a control parameter identification method for the Virtual Synchronous Generator (VSG) converter model considering the integration of electric vehicles (EVs) based on the dynamic particle swarm optimization algorithm is proposed. By constructing a VSG inverter control model suitable for distributed power sources and EV charging systems, analyzing the interactions between active and reactive power control loops under EV integration scenarios, selecting parameters and observations to be identified, and improving the Particle Swarm Optimization (PSO) algorithm based on actual conditions, the method ensures enhanced system adaptability. Simulation results demonstrate that the proposed method exhibits higher dynamic response capabilities, system stability, and adaptability under varying load conditions and uncertainties introduced by EV charging behaviors, highlighting its significant engineering application value.

Dynamic neighbourhood particle swarm optimisation algorithm for solving multi-root direct kinematics in coupled parallel mechanisms

In this study, we automatically estimated the parameters of the modified Cam-Clay model, a representative constitutive model for soil. The estimation was carried out by minimizing the objective function using the dynamic multiswarm particle swarm optimization (DMS-PSO) algorithm, which is an improvement over the original PSO. The objective function was newly defined by quantifying the discrepancy between the targeted results and the model calculations in q-p′-v space. DMS-PSO divides particles into several islands to search globally and prevent local solutions, and even particles that fall into a local solution can be relocated. To evaluate the automatic estimation performance of DMS-PSO, we examined whether model parameters could be correctly estimated from the calculation results (Consideration (1)) and whether the DMS-PSO algorithm could consistently obtain the same parameter values when reproducing the experimental results (Consideration (2)). Regarding Consideration (1), the objective function was consistently smaller than 1.0 × 10–6 when the number of particles was greater than 400 and the number of islands was greater than 40. At this time, the parameter values could be estimated to the fifth decimal place. When two experiments were conducted, the estimation was obtained approximately 1.5 times faster than when only one was conducted. Regarding Consideration (2), the coefficient of variation of the parameters obtained from 100 estimations was at most 1%, and the parameter values were estimated to be approximately the same each time. In addition, narrowing the solution search range based on soil physical properties could reduce the variation in parameters by approximately 10%. Additionally, the parameters could be accurately estimated by data from at least two mechanical experiments.

Purpose Accurate solar photovoltaic models (SPVM) are critical for optimizing solar photovoltaic (PV) capacity to convert sunlight into electricity. The simulation and design of PV systems rely on estimating unknown constraints from solar photovoltaic (SPV) cells. Each parameter plays a crucial task in the output properties of an SPV under actual environmental conditions. Optimizing the unknown constraints of the SPVM is not an easy task due to the nonlinear characteristics of the PV cell. This study aims to develop a novel metaheuristic algorithm, enhanced dynamic inertia particle swarm optimization (EDIPSO) algorithm with velocity clamping, to establish all the seven and five constraints of the two-diode model (TDM) and one-diode model (ODM). Design/methodology/approach In complex parameter spaces, the conventional particle swarm optimization (PSO) approach typically leads to poor convergence because it fails to balance exploration and exploitation. The proposed approach is an EDIPSO with velocity clamping to minimize the possibility of overshooting possible solutions and improve stability. Velocity clamping is also used to prevent particle velocities from rising over specified limitations. Beginning the process with a large inertia weight to promote exploration and progressively decreasing it to improve exploitation, leading to a thorough analysis of the search space. The algorithm is implemented to investigate the accuracy of estimated constraint values of RTC-France (RTC-F) solar cell, Photo watt-PWP 201 SPV module (PWP 201 SPV), KC 200GT SPV module (KC 200 GT SPV) for ODM and TDM. Findings The proposed approach is used to extract the seven and five constraints of the TDM and ODM under standard test conditions for three different SPV modules. Thorough simulation and statistical analysis indicate that the EDIPSO with velocity clamping may outperform other cutting-edge optimization algorithms exclusively regarding accuracy, computational time and reliability. Originality/value An enhanced dynamic inertia PSO is suggested for determining the parameters of the TDM and ODM in SPV modules. This method specifically accounts for the recombination saturation current within the p–n junction’s depletion region, without overlooking or assuming away any parameters, thereby achieving greater accuracy. When comparing the estimated constraints of TDM and ODM for various SPVs, EDIPSO almost precisely aligns the data from the proposed model with the practical data. Thus, the proposed method for calculating the SPV model parameter may exhibit to be a feasible and efficient solution.

Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation

Abstract This study describes an optimal method for deploying rescue ships in response to marine accidents using dynamic programming and particle swarm optimisation in an archipelago. We solved the shortest distance problem from a rescue ship to a marine accident using dynamic programming, which avoids obstacles, such as land or aquacultures. The optimal location problem is NP-hard. However, the optimal locations were found to be efficient among the various candidate combinations using particle swarm optimisation. We compared two models based on the set covering location model (SCLM) and P-median model (PMM). The PMM outperformed the SCLM approach in the test. The findings of this study may be valuable for directing judgments regarding search and rescue (SAR) vessel placements to maximise resource utilisation efficiency and service quality. Furthermore, this process can flexibly arrange multiple rescue ships.

A change severity degree-based dynamic multi-objective optimization algorithm with adaptive response strategy

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach.

ABSTRACT Understanding vehicles’ movement in complex environments becomes cruical with the fast development of connected automated vehicles (CAVs). Current microscopic traffic flow models lack consideration for vehicle dynamics and complex road topologies. This study develops a model addressing these issues, retrieving vehicles’ maneuvers and predicting vehicles’ two-dimensional motion. It introduces a two-dimensional intelligent driving model utilizing steering angle and acceleration as control inputs. Intricate road topologies are represented with potential fields and a virtual boundary to capture the heterogeneous environment’s complexity. A driving potential field model is also developed for off-ramp areas. Model parameters are optimized with the dynamic time warping (DTW) and particle swarm optimization (PSO) algorithm. Furthermore, model predictive control (MPC) enhances the realism of the model’s output. Field validation results demonstrate that the proposed models can accurately describe vehicles’ two-dimensional movement in intricate environments, offering valuable support for research on heterogeneous traffic flows for CAVs and CVs.

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Optimal Trajectory Planning for Robotic Arm Based on Improved Dynamic Multi-Population Particle Swarm Optimization Algorithm

A framework for dynamical distributed flocking control in dense environments