Classical Particle Swarm Optimization Research Articles

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

A novel hybrid method based MPP tracking design using boost converter for solar power systems

The convergence time is one of the key parameters for evaluating the energy conversion efficiency of PV power systems in the experimental process of tracking the maximum power point (MPP) in real-time. When the PV system is partially shaded, for example, MPP tracking easily slips into the local MPP and takes a long time. To overcome this drawback, a new solution for a stand-alone PV power system has been proposed that combines the incremental conductance (In-Cond) algorithm and the improved grey wolf optimization (GWO) approach. The improved GWO technique changed the methodology is used to find the global power area in this proposed method, and it is integrated with the In-Cond algorithm to fast obtain the global MPP. To demonstrate the efficacy of the suggested strategy, MATLAB/ Simulation and experiment results of the PV system are provided. The proposed hybrid method has a quick response time of 0.18 to 0.42 seconds for good transient oscillation and global power tracking, whereas the classic GWO and particle swarm optimization (PSO) methods take 0.82 to 2.1 seconds and 0.68 to 2.2 seconds, respectively. The global MPP was obtained not only with uniform irradiance intensity, but also with partial shade.

Applying Particle Swarm Optimization for Investigating the Stability of DC-DC Buck-Boost Converter: A Software Based Approach

ABSTRACT Stability of power converters has always been a major concern when dealing with nonlinear behavior of the system and swarm-based optimization techniques are proven to be handy as they are widely employed. Hence, this study presents an investigative approach to explore the compatibility of applying different variants of Particle Swarm Optimization (PSO) to design an optimized PID controller for DC-DC Buck-Boost converter. This work assesses converter stability through optimization of the PID controller using classical PSO (cPSO), Time-Varying Inertia Weight PSO (TVIW-PSO), Chaotic Descending Inertia Weight PSO (CDIW-PSO), Self-organizing Hierarchical PSO (HPSO) and Random inertia weight PSO (RIW-PSO). The State Space Average (SSA) method is utilized to model and attain the transfer function; eigenvalue analysis and time domain simulation analysis are considered to investigate the stability. Moreover, four objective functions and various performance parameters such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated to assess the system’s efficiency and the outcomes of different approaches are compared extensively. Amongst all, CDIW-PSO showcased better performances e.g. overshoot of 1.08%, settling time 0.000228s and rise time 0.000143s. The findings illustrate algorithm’s capacity to reduce transient and steady-state faults and makes it compatible with more nonlinear applications.

A Multiobjective Array Beamforming Method for Arrays of Flexible Shape

High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming.

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Stability and uncertainty assessment of geoelectrical resistivity model parameters: a new hybrid metaheuristic algorithm and posterior probability density function approach

Abstract. Estimating a reliable subsurface resistivity structure using conventional techniques is challenging due to the nonlinear nature of the inverse problems. The performance of the inversion techniques can be pretty ambiguous based on the optimal error, although traditional methods have proven to be quite effective. In this work, the impacts of the constraints accessible from a borehole are examined for further assessment and to enhance algorithm effectivity. The vPSOGWO strategy is a new approach that is based on a model search space without any prior information, and it describes the hybridization of particle swarm optimization (PSO) with the Grey Wolf Optimizer (GWO). To understand the efficiency and novelty of the algorithm, it has been validated on two different kinds of synthetic resistivity data with various sets of noise and, subsequently, applied to three field datasets of different geological terrains. The analyzed results suggest that the subsurface resistivity model shows considerable uncertainty. Thus, it is superior to examine the histograms and posterior probability density functions (PDFs) of such solutions to exemplify the global solution. A PDF with a 68.27 % confidence interval (CI) selects a region with a higher probability. Therefore, the inverted models are used to estimate the mean global solution and the most negligible uncertainties, where the mean global solution represents the best solution. Our vPSOGWO-inverted outcomes have been proven to be more accurate than classic PSO, GWO, and state-of-the-art variants of classic approaches. As a result, this novel method plays a vital role in vertical electrical sounding (VES) data inversion.

Exploring New Directions in Higher Education Aided by Artificial Intelligence Technology

Abstract This paper’s objective optimization problem belongs to the encoding problem of real numbers, and the classical particle swarm optimization algorithm is chosen. The establishment of intelligent learning model was finally completed through the assumption of model components, initial model establishment, model cycle evaluation and validation. First, after the implementation of the E-GPPE-C education model, the test was conducted on the educated people, and the AI literacy questionnaire was also distributed to the students before and after the implementation of the model education. Second, a statistical analysis explores which technologies will most likely be applied to higher education. Finally, the variability of higher education subject relations under the influence of AI and related ethical issues were explored. The results show that the highest score on the students’ final test was 96. 20 students scored more than 80 points, indicating that half of the students had a solid grasp of the knowledge and applications related to the E-GPPE-C model, and that there was a vast difference between the degree of AI knowledge and skill mastery before and after the implementation of education. Natural language understanding, computer vision, and biometrics are the three technologies most suitable for application in higher education.

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Optimal Energy Consumption Path Planning for Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization

In order to enhance the energy efficiency of unmanned aerial vehicles (UAVs) during flight operations in mountainous terrain, this research paper proposes an improved particle swarm optimization (PSO) algorithm-based optimal energy path planning method, which effectively reduces the non-essential energy consumption of UAV during the flight operations through a reasonable path planning method. First, this research designs a 3D path planning method based on the PSO optimization algorithm with the goal of achieving optimal energy consumption during UAV flight operations. Then, to overcome the limitations of the classical PSO algorithm, such as poor global search capability and susceptibility to local optimality, a parameter adaptive method based on deep deterministic policy gradient (DDPG) is introduced. This parameter adaptive method dynamically adjusts the main parameters of the PSO algorithm by monitoring the state of the particle swarm solution set. Finally, the improved PSO algorithm based on parameter adaptive improvement is applied to path planning in mountainous terrain environments, and an optimal energy-consuming path-planning algorithm for UAVs based on the improved PSO algorithm is proposed. Simulation results show that the path-planning algorithm proposed in this research effectively reduces non-essential energy consumption during UAV flight operations, especially in more complex terrain scenarios.

Novel IAPSO-LSTM neural network for risk analysis and early warning of food safety

Ensuring food quality and safety is essential after ensuring the quantity of food. Therefore, an improved adaptive particle swarm optimization algorithm (IAPSO) for optimizing the long short-term memory (LSTM) neural network (IAPSO-LSTM) is proposed to develop a food safety risk early warning model in this paper. The proposed IAPSO algorithm is verified by comparing with the traditional PSO (TPSO), the classic PSO (CPSO), the adaptive PSO (APSO) and the deterministic and adaptive PSO (DAPSO) based on five common benchmark functions in terms of the convergence speed and precision. Results show that the IAPSO algorithm has the best convergence speed and precision. Then, the analytic hierarchy process based on the sum product is used to obtain the risk value of food safety detection data, which is used as the desired output of the LSTM. And the root mean square error of the predicted risk value and the real food risk value is used as the objective function of the IAPSO algorithm to optimize the hyperparameters of the LSTM. Finally, the food safety risk early warning model based on the IAPSO-LSTM is evaluated using the composite seasoning detection data from a food testing agency in Guizhou Province, China. The proposed model effectively identifies high-risk factors affecting the risk value, with sodium cyclamate being the primary high-risk factor. The overall performance of the IAPSO-LSTM model is superior to that of the TPSO-based LSTM, the CPSO-based LSTM, the APSO-based LSTM, the DAPSO-based LSTM and the IAPSO-BP, with the prediction error of 0.474, R2 value of 0.998 and the absolute error less than 5. Moreover, the proposed model can help the relevant government departments effectively warn the potential food safety risks to ensure the food safety.

Optimization of a Multi-Type PMSM Based on Pyramid Neural Network

In this paper, a novel bat algorithm based on the quantum computing concept and pyramid neural network (PNN) is presented and applied to the electromagnetic motor optimization problem. Due to the problems of high loss, high temperature rise and threatening motor safety, it is necessary to optimize the design of high-speed permanent magnet synchronous motor (HPMSM) structure. In order to use less training data and avoid the problem of large computational costs due to repeated finite element simulation in the electromagnetic structure design, this paper adopted a performance-driven method to establish the PMSM model. This model could effectively reduce the dimensions of the parameter space and establish an effective high-quality model within a wide range of parameters. For the purpose of obtaining a reliable proxy model with less training data, this paper adopted a pyramid-shaped neural network, which could reduce the risk of overtraining and improve the utilization of specific problem knowledge embedded in the training data set. The quantum bat algorithm (QBA) was used to optimize the structure of the PMSM. Compared with the classical GA and PSO algorithms, the QBA has the characteristics of a rapid convergence speed, simple structure, strong searching ability and stronger local jumping mechanism. The correctness and effectiveness of the proposed PNN-based QBA method were verified using simulation analysis and a prototype test.

Method for Digital Cancellation of System Interference in a Full-Duplex Power Line Communication System

Every year the number of IoT devices is growing, which gives a boost to development of such technology as Power Line Communication (PLC). The term PLC refers to the use of a power supply circuit to transmit data. Full-duplex technology can be applied to increase the data transfer rate in a PLC. The difficulty in using a full duplex is the need to suppress the transmitter’s own signal (interference signal). Analog cancellation and digital cancellation are used for this purpose. Common algorithms for digital cancellation are adaptive algorithms. In this paper adaptive algorithms such as Particle Swarm Optimization (PSO) and Recursive Least Squares (RLS) are considered and a modification of the classical RLS is proposed. The proposed algorithm uses precalculated weight coefficients together with periodic reinitialization of the filter. This avoids the influence of significant changes in the input signal on the further process of filter adaptation and provides the highest level of echo suppression. The proposed algorithm provides an improvement of parameters such as Error Vector Magnitude (EVM) and Signal-to-Interference Ratio (SIR) in comparison with classical RLS and PSO, according to the results of the study.

Low-Cost Indirect Measurements for Power-Efficient In-Field Optimization of Configurable Analog Front-Ends with Self-X Properties: A Hardware Implementation

This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase.

Parameters and order identification of fractional-order epidemiological systems by Lévy-PSO and its application for the spread of COVID-19

To identify the knowledge about parameters and order is very important for the modeling of fractional-order epidemiological systems. In this paper, such an identification problem is formulated as a nonlinear optimization problem. For solving this, the Lévy-PSO algorithm, which is obtained by applying Lévy flight to generalize the classical particle swarm optimization (PSO), is used. More precisely, we first utilize Lévy-PSO to identify the constant parameters and the order of fractional-order SIR, SEIR systems with simulated data to show the effectiveness of our proposed identification strategy. Then, we continue employing Lévy-PSO to solve the parameter estimation problem of fractional-order SEAIR model under the real data of COVID-19 in Shanghai from 2/26/2022 to 4/27/2022. Numerical examples and associated comparisons with other existing methods allow us to achieve that our proposed identification strategy can generate a good performance with high accuracy and rapid convergence.

A meta-heuristic-based energy efficient route modeling for EVs integrating start/stop and recapturing energy effect

The research interest in electric vehicles (EVs) has recently sparked exponentially on account of curbing carbon emissions and paucity of fuel resources. No doubt, these vehicles are ecologically sustainable and move smoothly at a moderate speed that is economical on fuel, but their limited range due to the battery reserve demands an efficient use of energy resources. An immediate solution to overcome this problem lies in regulating the EV route, with optimum energy usage. Thus, this study presents a mathematical model that accounts for vehicle energy consumption with the vehicle’s start/stop energy expenditure. To achieve the objective of maximizing the energy-efficiency, it also introduces Amplified-ACO (A2CO), a routing algorithm based on Ant Colony Optimization (ACO) principles that make use of the probabilistic selection model (DS2 relies on Distance, Speed, and State-of-Charge). This study simulates the proposed model over the real-time map of Chandigarh (India) and provides a comparative study against some of the nature-inspired meta-heuristic approaches such as classical ACO and Particle Swarm Optimization (PSO). The proposed model justifies its significance under various parameters, viz. energy consumption, travel time, total travel distance, and remaining SoC.

Velocity pausing particle swarm optimization: a novel variant for global optimization

Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso.

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying. It is impractical to identify a model that can restore the model of real papermaking process. Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process. In this study, a strange nonchaotic particle swarm optimization (SNPSO) algorithm is proposed to identify the models of real papermaking processes, and this identification ability is significantly enhanced compared with particle swarm optimization (PSO). First, random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions. Furthermore, the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO. The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization. To illustrate the effectiveness of SNPSO, step response data are used to identify the models of real industrial processes. Compared with classical PSO, PSO with time-varying acceleration coefficients (PSO-TVAC) and modified particle swarm optimization (MPSO), the simulation results demonstrate that SNPSO has stronger identification ability, faster convergence speed, and better robustness.

Optimal Placement Identification of Multiple DG Types Using Optimization Technique

In this paper, a combination algorithm called GAIPSO, which combines GA and a better version of the classic particle swarm optimization process, is used. In order to calculate the data enhancement in voltage profile, this study uses the GWO algorithm. The ideal position for the proposed charging points inside the distribution system is the goal. The received comment thread solution (site & station size) is further re-optimized by PSO, improving both the functionality and outcome overall. Studies based on simulations show that the above mentioned technique outperforms GA, GWO, and PSO in respect of an improved voltage profiles as well as the quality of the solution found for the objective function. Optimum planning for the charging station’s location and size. the IEEE 33 bus system. The suggested approach takes into consideration the IEEE 33 bus service. The received thread solutions (site and station size) is further re-optimized by PSO, improving both the performance and outcome overall.

A new hybrid optimization approach using PSO, Nelder-Mead Simplex and Kmeans clustering algorithms for 1D Full Waveform Inversion.

The FWI is formulated as a nonlinear optimization problem that traditionally uses local (derivative-based) minimization to find the scalar field of properties that best represents the field seismic data. This problem has a high computational cost and accuracy limited to local minima, in addition to suffering from a slow convergence rate (Cycle Skipping). Therefore, we developed a two-phase hybrid optimization algorithm based on DFO algorithms. The first use global minimization and clustering technique. The second use local minimization. In phase 1 we adopted the modified PSO and K-means algorithms and in phase 2, we adopted the ANMS. We call the hybrid algorithm of the PSO-Kmeans-ANMS. Where K-means is responsible for dividing swarms of particles into 2 clusters at every instant. This strategy aims to automatically balance the mechanisms of exploration and exploitation of the parameter search space by the hybrid algorithm, allowing one to find more precise solutions and consequently improving its convergence. The PSO-Kmeans-ANMS algorithm was validated on the set of 12 benchmark functions and applied to the FWI 1D problem. We compared PSO-Kmeans-ANMS with classic PSO, modified PSO, and ANMS algorithms. The metrics used were are the average execution time and the success rate (an error of ± 4% of the optimal solution). In all validation experiments and the FWI application, the PSO-Kmeans-ANMS performed well in terms of robustness and computational efficiency. In the case of FWI, there was a significant reduction in computational cost, thus presenting a relevant result.

Particle swarm optimization with Chebychev functional-link network model for engineering design problems

In recent years, many improved particle swarm optimization (PSO) algorithms have been developed to improve the performance of PSO. These improved algorithms have greatly improved PSO performance, but PSO still has some shortcomings. So, an particle swarm optimization with Chebychev functional-link network model is proposed (APSOCFLN) in this paper. Firstly, in order to make up for the shortcomings of the canonical PSO update search, a novel Chebychev functional-link network (CFLN) elite guidance strategy is proposed. Two different update mechanisms are executed alternately, increasing the diversity of the algorithm population and making the algorithm more capable of jumping out of local optimization. Secondly, in order to better balance the exploration and exploitation of the algorithm, an adaptive probability strategy is proposed. Thirdly, an adaptive weighting strategy is proposed. It will be used for CFLN elite guidance strategy and give different weights to the two alternating update strategies, which can effectively solve the problem of premature convergence of PSO. Fourthly, the APSOCFLN and comparison algorithms are used to solve the practical engineering problems, and the results show that APSOCFLN has high precision, fast convergence , and excellent performance. Finally, the performance of the algorithm is tested with CEC2017 and CEC2022 benchmark functions. The comparative algorithm includes the classical PSO improvement algorithms and the classical other algorithms, and the experimental results show the effectiveness of the proposed strategy and the good performance of the improved algorithm.