Particle Swarm Optimization Algorithm Research Articles

1 × 2 broadband polarization-independent beam splitter based on air annular photonic crystal designed by a particle swarm optimization algorithm

We propose a 1×2 broadband polarization-independent beam splitter based on air annular photonic crystals. The inner and outer radii of the air annular photonic crystals at the V-shaped waveguide side and corners can influence the transmittance and broadband performance. To improve the design efficiency and transmittance of the broadband splitter, particle swarm optimization was used to reverse-design the splitter. The performance of the designed splitter was studied, and the results show that the designed splitter has a wide bandwidth range of 1530–1565 nm. It exhibits a minimum transmittance of 84% and an average transmittance of 87.8% for TE polarization and a minimum transmittance of 81% and an average transmittance of 83% for TM polarization. The response times for both polarizations are below 0.7 ps. This device has promising applications in all-optical communication networks and photonic high-density integration.

Advanced optimization of elastic sheets for solar parabolic trough concentrators: integrating particle swarm optimization and genetic algorithms

Abstract The fabrication of solar parabolic trough concentrators using flat elastic sheets presents a straightforward and cost-effective method. This paper introduces an optimization technique centered on stiffness adjustment, harnessing elastic buckling to attain precise parabolic shapes in these concentrators. Through an enhanced Particle Swarm Optimization-Genetic Algorithm (PSO-GA), strategically punched holes are optimized on the flat sheet, allowing for the attainment of perfect parabolic shapes by controlling the chord length with a positional rod or cable. The efficacy of this approach is showcased not only through interactive finite element analysis and ray tracing software simulations but also via experimental sunlight concentration. A geometric concentration ratio of up to 145.16 is achieved, underscoring the effectiveness of this innovative concept. This approach facilitates the simple fabrication and transportation of flat mirror elements to field sites, where they can be assembled into parabolic trough concentrators, offering potential cost reductions and highly efficient solar energy solutions.

The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids

The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.

Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the optimization process. We present a novel approach to design such adaptation strategies using continuous fuzzy feedback control. Fuzzy feedback provides a simple interface where probes are sampled in the optimization process and parameters are fed back to the optimizer. The probes are turned into parameters by a fuzzy process optimized beforehand to maximize performance on a training benchmark. Utilizing this framework, we systematically established 127 different Fuzzy Particle Swarm Optimization algorithms featuring a maximum of 7 parameters under fuzzy control. These newly devised algorithms exhibit superior performance compared to both traditional PSO and some of its best parameter control variants. The performance is reported in the single-objective bound-constrained numerical optimization competition of CEC 2020. Additionally, two specific controls, highlighted for their efficacy and dependability, demonstrated commendable performance in real-world scenarios from CEC 2011.

Optimization of mixture ratios of raw materials in thermoplastic hybrid composites based on particle swarm optimization algorithm

Optimization of mixture ratios of raw materials in thermoplastic hybrid composites based on particle swarm optimization algorithm

Current Stress Minimization Based on Particle Swarm Optimization for Dual Active Bridge DC–DC Converter

Under extended-phase-shift (ESP) control, the current stress of the dual active bridge converter (DAB) is relatively high, which reduces the efficiency of the converter. To solve this problem, a particle swarm optimization (PSO) algorithm based on minimizing the current stress is proposed in this paper. The optimal phase-shift ratio of the DAB converter with ESP control is obtained by using the algorithm’s optimization characteristic. This approach ensures that the converter achieves minimal current stress, thereby enhancing the steady-state performance of the DAB converter. Moreover, in terms of dynamic performance, traditional PI control has poor dynamic response ability when there are sudden changes in load and input voltage. To solve this problem, the voltage dynamic matrix control (DMC) algorithm is introduced to combine with the PSO algorithm to minimize the current stress of the DAB converter under EPS control while enhancing the dynamic response capability of the DAB converter. A simulation model was constructed for comparative validation on MATLAB/Simulink 2019, demonstrating the correctness and effectiveness of the improved control method.

Updating assembly parameters for spacecraft assembly process state changes: based on data fusion method

Thermal protection systems (TPSs) are important components of reusable spacecraft, and their assembly quality has a crucial impact on flight safety. Owing to the complex assembly process and variable states of spacecraft thermal protection systems, assembly parameters may vary under different assembly states. Therefore, to obtain assembly parameters accurately and efficiently under different assembly states, in this study, 3D point cloud data and fiber optic sensor data were fused to develop an assembly parameter update method for assembly process state changes. Firstly, based on the measured data of thermal protection components and load-bearing structure, the gap, flush and matching parameters solution model are proposed. Secondly, to address the deformation problem of the load-bearing structure caused by changes in assembly status, a fusion method based on laser scanning and sensor detection was devised to achieve deformation prediction of the assembly structure during the assembly process. Thirdly, based on the assembly parameter solution model and point cloud prediction model, a constraint-based assembly parameter optimisation model was established, and an improved quantum particle swarm optimisation (LQPSO) algorithm was employed to achieve assembly parameter updates oriented toward changes in assembly status. Finally, an experimental system for array-based thermal protection structure simulation was established to validate the proposed method. The results show that the proposed parameter update method can achieve ideal results for different assembly state simulation components.

Enhancement of Active Distribution Network Performance with Multiple Distributed Generators and DSTATCOMs using Reptile Search Algorithm

The integration of multiple DGs can introduce power quality issues and instability in the (DN) due to their intermittent and fluctuating nature. Distributed Static Compensators (DSTATCOMs) are devices that effectively manage and regulate both active and reactive powers, thereby maintaining the desired levels of reactive power in the DNs. This paper investigates the problem of determining the optimal number and placement of multiple DSTATCOMs within active DNs with multiple Distributed Generators (DGs) connected to them. To achieve the optimal sizing and allocation of multiple DSTATCOMs, a novel heuristic method called the reptile search algorithm (RSA) is introduced in this study. A combination of the RSA method and the loss sensitivity factor (LSF) are utilized to identify the optimal number, sizes, and locations of DSTATCOMs to enhance the performance of active DNs with different types of DGs and loads. The desired improvements include mitigating voltage deviation at nodes, minimizing system power losses, and alleviating overloading in feeders. The effectiveness of the algorithm is evaluated using an IEEE-33 bus DN, which is modified to incorporate different DGs and load types. To assess the efficiency of the proposed method, a modified particle swarm optimization (MPSO) algorithm is used for comparison. The results demonstrate that the RSA approach presented in this paper is robust in obtaining optimal solutions, offers fast and easy implementation, can be applied to large DNs, and outperforms the MPSO algorithm.

Location of 5G base station antenna in substation taking into account path loss and RF radiation

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

An adaptive assisted method based on MOPSO for THz MMA effective designing

Abstract To address the high time cost and low efficiency of traditional terahertz metamaterial absorber design, an adaptive method based on multi-objective particle swarm optimization was proposed. First, we presented a symmetric absorber of a four-split-ring resonator with a cross-shaped top layer. The structural parameters were optimized by the particle swarm optimization and multi-objective particle swarm optimization algorithms. The simulation results indicated that the multi-objective particle swarm optimization rapidly obtained geometric parameters that balance the high absorptivity and high quality factors. This method quickly identified a symmetric structure with absorptivity greater than 99% and quality factor of 377.45 at 1.584 THz. Additionally, the asymmetric structure achieved near-perfect absorption at 1.585 THz with a Q value of 281.32. By studying the electric field distribution and the surface current distribution, the physical mechanism of the absorber designed has been explained. This adaptive assisted method based on multi-objective particle swarm optimization can efficiently design metamaterial absorbers and offer an artificial intelligence strategy for terahertz functional device design.

Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms

Carbonation is a significant factor contributing to the instability of concrete structures. This study proposes two prediction models based on the gradient boosting decision tree (GBDT) to address the challenge of predicting concrete carbonation depth. This study integrates GBDT with two metaheuristic algorithms, particle swarm optimization algorithm (PSO) and sparrow search optimization algorithm (SSA), forming two hybrid models, PSO-GBDT and SSA-GBDT, aimed at enhancing prediction accuracy. Six influencing parameters (FA, t, w/b, B, RH, and CO2) were selected as input features to train and evaluate the hybrid models, with the concrete carbonation depth was used as the output. A database containing 883 groups of cases was established. Finally, three classic models, GBDT, ANN and SVR, were compared against the two hybrid models. To enhance model generalization, all models were subjected to five-fold cross validation. Four evaluation indicators (RMSE, R2, MAE, and VAF) and Taylor diagrams were employed to comprehensively assess these models. The results indicated that the two hybrid models exhibited superior prediction performance in the dataset (training set: R2, VAF>0.98, testing set: R2, VAF>0.96). The SSA-GBDT model achieved the highest prediction performance (RMSE= 2.7008, R2= 0.9639, MAE= 1.7691, VAF= 0.9627). Additionally, the SSA-GBDT model identified exposure time (t) as the feature with the greatest impact. This study demonstrates the applicability of the SSA-GBDT models in predicting concrete carbonation depth, offering a novel approach for improving accuracy in such predictions.

An optimal approach for active power reserve of a de-loaded PV generator operating in coordination with synchronous generators

The de-loading operation of PVs, where PVs are operated with an active power reserve has picked up the interest of the researchers in the last few years. However, in an integrated system, where the PV generators are operated in coordination with the synchronous generators, the estimation of the amount of the active power reserve of the PV generator is crucial. A very high reserve will cause an excessive economical loss and a very low reserve will deteriorate the frequency regulation capability. Therefore, in this paper, the cost function of an integrated system is minimised using the particle swarm optimisation algorithm, and the amount of power reserve of the PV generator and operating power values of synchronous generators are obtained as a result of the minimisation. Further, the frequency response of the system for the obtained power reserve value is investigated by suddenly adding the load in the system. The lowest and highest system costs of 2521.87 and 4474.98 $/h are obtained for the irradiance 1000 and 400 W/m2 respectively, and upon a significant load change, the frequency deviation limits to 0.4513 for a reserve value of 29.0712 MW. System modelling and validation have been carried out in the SIMULINK toolbox of the MATLAB software.

Research on IP Node Port Openness Prediction Method Based on PSO-CatBoost

The development of network measurement technologies has greatly increased the speed of network scans, but it also poses risks for the stability of the scanned networks. How to reduce probing traffic and enhance the effectiveness of probing has become a new research issue. In this paper, we utilize network measurement and machine learning techniques, leveraging public interfaces from network mapping platforms to construct a dataset with 44 feature dimensions. By combining the categorical boosting (CatBoost) model with the particle swarm optimization (PSO) algorithm for heuristic optimization, we propose a host port openness prediction model that integrates the PSO algorithm and the CatBoost model. Through comparisons with various machine learning models, the effectiveness of our proposed model was validated. Using this model in network scanning can save approximately 65% of bandwidth on average, effectively reducing the impact on the probed network.

Forecasting urban water demand using different hybrid-based metaheuristic algorithms’ inspire for extracting artificial neural network hyperparameters

This research offers a novel methodology for quantifying water needs by assessing weather variables, applying a combination of data preprocessing approaches, and an artificial neural network (ANN) that integrates using a genetic algorithm enabled particle swarm optimisation (PSOGA) algorithm. The PSOGA performance was compared with different hybrid-based metaheuristic algorithms’ behaviour, modified PSO, and PSO as benchmarking techniques. Based on the findings, it is possible to enhance the standard of initial data and select optimal predictions that drive urban water demand through effective data processing. Each model performed adequately in simulating the fundamental dynamics of monthly urban water demand as it relates to meteorological variables, proving that they were all successful. Statistical fitness measures showed that PSOGA-ANN outperformed competing algorithms.

Optimization for container truck routing in container terminal with multi quay cranes considering emissions policy

In container terminal, the container trucks should go by a “quay crane - container trucks -import and export block” routing, and the optimization of this routing has become the focus of this paper. This study divided the problem of routing optimization into two levels: one is a routing optimization model of single container truck which takes the lowest total cost as the target, without considering the distribution of overall transportation task; the other is a routing optimization model concerning the allocation of no-load trucks, aiming at the lowest cost of transportation task. We designed an improved Particle Swarm Optimization (PSO) algorithm to solve the two-level optimization model, and the influence of emission policy on the two-level optimization model and the influence of the number of container trucks on the transportation distance of level II in optimization model are analyzed. The results show that the increase of carbon tax will lead to the decrease of emissions in level I, but has no effect on the emissions in level II. The increase in the number of container trucks will lead to a reduction of the no-load transportation distance in level II.

Multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization for expensive optimization problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to solve computationally expensive optimization problems. However, most SAEAs struggle to achieve good results in solving complex multimodal problems, especially high-dimensional ones. Moreover, for problems with complex landscapes, SAEAs typically require constructing complex global surrogates to model the landscape and performing many iterations to identify the surrogate’s optimum, thereby reducing the efficiency of SAEAs. To deal with these issues, this paper proposes a multi-region hierarchical surrogate-assisted quantum-behaved particle swarm optimization (MHS-QPSO) algorithm for expensive optimization problems. To better balance exploration and exploitation, a search behavior selection strategy is proposed, enabling MHS-QPSO to appropriately switch between global and local searches. For the global search, the search space is divided into multiple regions that can adaptively adjust the size of the areas. A surrogate is constructed in each region, requiring only a small number of QPSO iterations to find the optimum of each surrogate. Furthermore, a novel reliability-based criterion is proposed to screen candidate solutions in different regions for exact evaluations, which can save the number of exact function evaluations and can rapidly improve the fitting accuracy of the surrogates in regions with superior fitness. During local searches, a dynamic boundary adjustment strategy is introduced to guide the QPSO to faster approach the potential optimal region. Experimental results on seven benchmark functions with dimensions from 10 to 100, and on a complex real application, demonstrate that MHS-QPSO significantly outperforms several state-of-the-art algorithms within a limited computational budget. Code for MHS-QPSO is available at https://github.com/quanshuzhang/MHS-QPSO.git.

Robot inverse kinematics solution for center selection battle royale optimization algorithm

In order to solve the problems of high time complexity and insufficient global exploration ability of the battle royale optimization algorithm, this paper proposes an improved battle royale optimization algorithm based on chaos mapping, center selection and elite adaptive strategy. Through benchmark function testing, compared to particle swarm optimization algorithm, whale optimization algorithm, and the battle royale optimization algorithm, the improved algorithm significantly reduces time complexity and notably enhances convergence precision, speed, and stability. The improved algorithm is applied to the inverse kinematics problem of robots. The accuracy and The stability of the improved algorithm is better than those of the traditional battle royale optimization algorithm, it demonstrates superior solving precision and stability, proving its practicality and potential for development in solving robot inverse kinematics problems.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

A fault-tolerant algorithm of AUV formation based on reconfiguration map

Aiming at the fault occurrence of AUV formation members during sailing, a fault tolerance algorithm of AUV formation based on reconfiguration was proposed. Firstly, the cost matrix is designed based on the reconfiguration and Hungarian algorithm to solve the problem of assigning target points under the fault condition. Then, based on Bezier curves, the dynamic constraints of AUV are designed parametrically. The nonlinear optimization problem satisfies the constraints through the characteristics of spline curves, and the adaptive particle swarm optimization algorithm is used to solve the tracking trajectory. The comparison and simulation experiment of a particle swarm algorithm based on a reconfiguration map (RMPSO) was carried out. The simulation results show that under the same planned path, the total tracking path of the following AUV is shorter, the turning radius is smaller, and the heading Angle is more stable. Finally, field tests are carried out in Qiandao Lake to verify the effectiveness of the algorithm for formation faults.