Particle Swarm Optimization Algorithm Research Articles

Research on the Prediction Model of Coal Spontaneous Combustion Hazard Level Based on IGWO-GRNN

ABSTRACT To accurately predict the risk level of spontaneous coal combustion, we introduce an improved grey wolf optimization strategy (IGWO) to optimize the generalized regression neural network (GRNN), thereby establishing the IGWO-GRNN coal spontaneous combustion risk level prediction model. The superiority of the IGWO algorithm over the grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) was verified. Fifty sets of sample data of spontaneous coal combustion in the mining area were selected as the research object, and the data were randomly divided into the training set and the test set according to the ratio of 8:2. The prediction results of the IGWO-GRNN prediction model were obtained and compared with those of the GWO-GRNN, GRNN, BP (Backpropagation), and GRU (Gated Recurrent Unit) models, and the absolute value of the relative error was introduced as an evaluation index of the model prediction accuracy. The results demonstrate that IGWO outperforms GWO, WOA, and PSO in terms of optimization efficiency, with the IGWO-GRNN prediction model yielding the most accurate results. Specifically, the maximum absolute value of the relative error does not exceed 4.27%, and the average relative error is only 3.29%, indicating a substantial improvement over the other models. Therefore, this study achieves high-precision prediction under small-sample data, which provides an important reference value for the small-sample prediction problem in the mining field.

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique

Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. Diabetic Retinopathy (DR) is a kind of eye disease was associated with increasing risk factors for all-causes of CVD events. The early diagnosis of DR plays a significant role in preventing CVD. However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. The proposed work introduces Improvised Deep Belief Network named I-DBN to resolve the above mentioned problems and mainly to concentrate on improving the entire performance of the model leading to the unbiased output. We used Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) algorithm for feature extraction and selection respectively. Five performance metrics have been used to assess the proposed model. The results of I-DBN outperform other state-of-the-art methods. The result validation ensures that I-DBN can deliver trustworthy recommendations to doctors to treat the patients by enhancing the accuracy of CVD prediction up to 98.95%.

Research on Sensitivity Improvement Methods for RTD Fluxgates Based on Feedback-Driven Stochastic Resonance with PSO

With the wide application of Residence Time Difference (RTD) fluxgate sensors in Unmanned Aerial Vehicle (UAV) aeromagnetic measurements, the requirements for their measurement accuracy are increasing. The core characteristics of the RTD fluxgate sensor limit its sensitivity; the high-permeability soft magnetic core is especially easily interfered with by the input noise. In this paper, based on the study of the excitation signal and input noise characteristics, the stochastic resonance is proposed to be realized by adding feedback by taking advantage of the high hysteresis loop rectangular ratio, low coercivity and bistability characteristics of the soft magnetic material core. Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. The simulation results show that optimizing the odd polynomial feedback coefficients with PSO enables the sensor to reach a resonance state, improving sensitivity by at least 23.5%, effectively enhancing sensor performance and laying a foundation for advancements in UAV aeromagnetic measurement technology.

Hybrid multi-objective optimization of µ-synthesis robust controller for frequency regulation in isolated microgrids

Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability—a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller’s effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.

Collaborative Optimization of Direct Current Distribution Network Based on Scaled Electric Vehicles Charging and Discharging and Soft Open Points Topology Reconfiguration

In order to reduce the impact of the performance degradation of a direct current (DC) distribution network system caused by the access of scaled electric vehicles (EVs), this paper proposes a collaborative optimization method for a DC distribution network based on scaled EVs charging and discharging and soft open points (SOPs) topology reconfiguration. Firstly, based on the normal charging of scaled EVs, the EV discharge power model and the discharge response model were established based on the V2G (vehicle-to-grid) characteristic. Based on the characteristics of SOPs regulating voltage and power distribution, the SOP model and its equivalent model of DC system are established to identify the collaborative optimization of scaled EVs charging and discharging and SOPs topology reconstruction. Secondly, the bi-level model that optimizes multi-objects, including distribution network system loss, total voltage deviation and customer benefits, is established. The upper and lower models use the multi-objective particle swarm optimization (MOPSO) algorithm and simulated annealing algorithm to jointly optimize the optimal EV discharge power and the optimal SOP access configuration simultaneously. Finally, the effectiveness of the proposed collaborative optimization method is verified by a modified IEEE 33-node DC system example.

A spherical vector-based adaptive evolutionary particle swarm optimization for UAV path planning under threat conditions

Unmanned aerial vehicle (UAV) path planning is a constrained multi-objective optimization problem. With the increasing scale of UAV applications, finding an efficient and safe path in complex real-world environments is crucial. However, existing particle swarm optimization (PSO) algorithms struggle with these problems as they fail to consider UAV dynamics, resulting in many infeasible solutions and poor convergence to optimal solutions. To address these challenges, we propose a spherical vector-based adaptive evolutionary particle swarm optimization (SAEPSO) algorithm. This algorithm, based on spherical vectors, directly incorporates UAV dynamic constraints and introduces improved tent map and reverse learning to enhance the diversity and distribution of initial solutions. Additionally, dynamic nonlinear and adaptive factors are integrated to balance exploration and exploitation capabilities. To avoid local optima in highly complex environments, we propose an adaptive acceleration strategy for poor particles, and an evolutionary programming strategy is incorporated to further improve the optimization capability. Finally, we conducted comparative studies and in six benchmark scenarios with varying threat levels, and the results demonstrated that the proposed algorithm outperforms others in the initial solution effectiveness, the final solution accuracy, convergence stability, and scalability.

Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework

Financial distress prediction (FDP) is a critical area of study for researchers, industry stakeholders, and regulatory authorities. However, FDP tasks present several challenges, including high-dimensional datasets, class imbalances, and the complexity of parameter optimization. These issues often hinder the predictive model’s ability to accurately identify companies at high risk of financial distress. To mitigate these challenges, we introduce FinMHSPE—a novel multi-heterogeneous self-paced ensemble (MHSPE) FDP learning framework. The proposed model uses pairwise comparisons of data from multiple time frames combined with the maximum relevance and minimum redundancy method to select an optimal subset of features, effectively resolving the high dimensionality issue. Furthermore, the proposed framework incorporates the MHSPE model to iteratively identify the most informative majority class data samples, effectively addressing the class imbalance issue. To optimize the model’s parameters, we leverage the particle swarm optimization algorithm. The robustness of our proposed model is validated through extensive experiments performed on a financial dataset of Chinese listed companies. The empirical results demonstrate that the proposed model outperforms existing competing models in the field of FDP. Specifically, our FinMHSPE framework achieves the highest performance, achieving an area under the curve (AUC) value of 0.9574, considerably surpassing all existing methods. A comparative analysis of AUC values further reveals that FinMHSPE outperforms state-of-the-art approaches that rely on financial features as inputs. Furthermore, our investigation identifies several valuable features for enhancing FDP model performance, notably those associated with a company’s information and growth potential.

Optimization of Urban Fire Emergency Resource Allocation Based on Pre-Allocated Swarm Algorithm

As a high-frequency disaster with potentially devastating consequences, urban fires not only threaten the lives of city residents but can also lead to severe property losses, especially for hazardous chemical leaking scenarios. Quick and scientific decision-making regarding resource allocation during urban fire emergency responses is crucial for reducing disaster damages. Based on several key factors such as the number of trapped individuals and hazardous chemical leaks during the early stages of an incident, an emergency weight system for resource allocation is proposed to effectively address complex situations. In addition, a multi-objective optimization model is built to achieve the shortest response time for emergency rescue teams and the lowest cost for material transportation. Additionally, a pre-allocated bee swarm algorithm is introduced to mitigate the issue of local incident points being unable to participate in rescue due to low weights, and a comparison of traditional genetic algorithms and particle swarm optimization algorithms is conducted. Experiments conducted in a virtual urban fire scenario validate the effectiveness of the proposed model. The results demonstrate that the proposed model can effectively achieve the dual goals of minimizing transportation time and costs. Furthermore, the bee swarm algorithm exhibits advantages in convergence speed, allowing for the faster identification of ideal solutions, thereby providing a scientific basis for the rapid allocation of resources in urban fire emergency rescues.

A novel group-based framework for nature-inspired optimization algorithms with adaptive movement behavior

This paper proposes two novel group-based frameworks that can be implemented into almost any nature-inspired optimization algorithm. The proposed Group-Based (GB) and Cross Group-Based (XGB) framework implements a strategy which modifies the attraction and movement behaviors of base nature-inspired optimization algorithms and a mechanism that creates a continuing variance within population groupings, while attempting to maintain levels of computational simplicity that have helped nature-inspired optimization algorithms gain notoriety within the field of feature selection. Through this functionality, the proposed framework seeks to increase search diversity within the population swarm to address issues such as premature convergence, and oscillations within the swarm. The proposed frameworks have shown promising results when implemented into the Bat algorithm (BA), Firefly algorithm (FA), and Particle Swarm Optimization algorithm (PSO), all of which are popular when applied to the field of feature selection, and have been shown to perform well in a variety of domains, gaining notoriety due to their powerful search capabilities.

Design of Power Economic Dispatch Method Integrating Differential Evolution and PSO

As energy demand continues to grow and environmental problems become increasingly serious, optimizing the economic dispatch of the power system is crucial to ensuring the sustainability and economic benefits of energy supply. To ensure the safe operation of the power system, reduce power generation costs as much as possible, and develop a method that can adapt to the needs of different power systems, the experiment combines the differential evolution algorithm with the power economic dispatch problem and proposes a method based on improved differential evolution. Electric power economic dispatch method with particle swarm optimization algorithm. The experiment first introduces a moderate interference strategy to appropriately adjust the position of the particles; then combines the local mutation strategy to enhance the searchability of the differential evolution algorithm in the solution space and achieve good economic dispatch. The results show that when running on the F11 test set and F21 test set, when the system iterates to the 26th and 32nd times respectively, the loss function of the method constructed in the experiment begins to have a minimum value and remains stable thereafter. In addition, on the F11 test set, when the number of iterations is 150, this method has a minimum time of 0.153s. While running the loop for the first time on System 1, the total cost of this approach was only $1.01× 104. Through the actual operation of power generation equipment, under the operation of this method, the power system can ensure optimal operating power of each power generation equipment unit based on ensuring optimal cost. It can be seen from the above results that this method provides power system operators and decision-makers with a new tool to help them maximize cost-effectiveness while ensuring system stability and meeting power demand. In addition, the method's superior convergence and stability can effectively improve the solution's accuracy and speed and has strong practicality and promotion value.

Compact High-Zoom-Ratio Mid-Wavelength Infrared Zoom Lens Design Based on Particle Swarm Optimization

This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lenses under paraxial conditions is investigated, and a model for the focal power distribution and relative motion of three movable lens groups is established. The particle swarm optimization (PSO) algorithm is introduced into the zooming process analysis, and a program is developed in MATLAB to solve for the initial structure. This algorithm integrates physical constraints from lens analysis and evaluates candidate solutions based on key design parameters, such as total lens length, zoom ratio, Petzval field curvature, and focal length at tele end. The results demonstrate that the proposed method can efficiently and accurately determine the initial structure of compact MWIR zoom lenses. Using this method, a mid-wave infrared zoom lens with a zoom ratio of 50×, a total length of less than 530 mm, and the ratio of focal length to total length approximately 2:1 was successfully designed. The design validates the effectiveness and practicality of this method in solving the initial structure of zoom lenses that meet complex design requirements.

Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques—GMDH-type neural network, gene expression programming, and combinatorial algorithm—are applied to model dynamic viscosity and thermal conductivity as functions of the input variables. Then, the high-performing models provide the foundation for optimization using the well-established multi-objective particle swarm optimization algorithm. Finally, the decision-making technique TOPSIS is employed to identify the most desirable points from the Pareto front, based on various design scenarios. To validate the proposed strategy, a ternary hybrid nanofluid composed of graphene oxide (GO), iron oxide (Fe₃O₄), and titanium dioxide (TiO₂) was employed as a case study. The results demonstrated that the combinatorial approach excelled in accurately modeling (R = 0.99964–0.99993). The optimization process revealed that optimal VFs span a broad range across all mixing ratios, while optimal temperatures were consistently near the maximum value (65 °C). The decision-making outcomes indicated that the mixing ratio was consistent across all design scenarios, with the volume fraction serving as the key differentiating factor.

Capacity optimization of photovoltaic storage hydrogen power generation system with peak shaving and frequency regulation

To solve the problem of power imbalance caused by the large-scale integration of photovoltaic new energy into the power grid, an improved optimization configuration method for the capacity of a hydrogen storage system power generation system used for grid peak shaving and frequency regulation is proposed. A hydrogen storage power generation system model is established, and the photovoltaic power generation and hydrogen fuel cell power generation is calculated. A two-layer hydrogen storage power generation system capacity optimization configuration model was established, an improved particle swarm optimization algorithm was used to solve the improved hydrogen storage power generation system capacity optimization configuration model, and the capacity optimization configuration results were obtained. The experimental results show that this method can obtain the best capacity allocation result based on local lighting conditions and the cost of the power generation system, whereas the improved particle swarm optimization algorithm can solve the model faster and reduce the cost of the power generation system. After applying this method, the net income of the solar hydrogen storage power generation system has almost doubled.

Optimization of Microgrid Dispatching by Integrating Photovoltaic Power Generation Forecast

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system.

Simultaneous measurement of particle size distribution and mixing ratio based on Monte Carlo ultrasonic attenuation model

Abstract Mixed particle systems are commonly employed in industrial processes, where the characterization of particle size parameters and mixing ratio can frequently serve as key indicators in process control and production optimization. A Monte Carlo (MC) model was developed to numerically predict and study the ultrasonic attenuation spectrum characteristics in the polymethyl methacrylate (PMMA)-glass aqueous suspension, and together with the particle swarm optimization (PSO) algorithm, to handle the inverse problem in solving the particle size, distribution width, and mixing ratio. The results of the numerical simulations indicate that there exists a linear relationship between the attenuation coefficient and the mixing ratio, with the particle size exerting a significant influence. Furthermore, the multi-parameter simultaneous inversion also yielded calculation deviations of less than 1%, 3%, and 6% for the mixing ratio, characteristic diameter, and distribution width, respectively, in comparison to their given values. Afterward, a series of experiments were conducted to quantify the particle size and mixing ratio through the analysis of ultrasonic spectra. In spherical PMMA-glass aqueous suspensions, the measurement error for the mixing ratio and particle size parameters are found to be less than 7% and 10%, respectively, when compared to the image method and the given values. Nevertheless, the measurement errors are slightly increased in a non-spherical mixed particle system, where the volume median diameter and mixing ratio are still less than 10%. The MC modeling and PSO algorithm offer the potential to characterize particle size and mixing ratio for mixed particle systems in industrial applications.

Two competitive hybridization approaches based on combining of Giza Pyramids Construction with Particle Swarm Optimization for solving global optimization problems

Optimization problems are complex problems that are very difficult to solve. Although these types of problems are solved in the real world using exact methods, these methods are very time-consuming and costly. By using soft computing methods, the time and cost of problem-solving can be reduced to some extent. Engineering problems are among the complex real-world problems that can be solved through soft computing methods. One of these methods is the use of metaheuristic algorithms to optimize the solution of these types of problems. The Particle Swarm Optimization (PSO) algorithm is a common and state-of-the-art metaheuristic algorithm used to solve engineering optimization problems. This algorithm is known as swarm-based optimization techniques and has a very powerful mathematical basis. Another recently published algorithm is the Giza Pyramids Construction (GPC) algorithm. The GPC algorithm models the technological advancements of construction in ancient times. Both algorithms have many advantages through which optimization problems can be solved effectively. To increase the power of metaheuristic algorithms and solve optimization problems more effectively through them, the idea of competitive hybridization algorithms has been proposed. In this paper, two competitive hybrid approaches of combining PSO and GPC algorithms are presented. These two competitive hybridization approaches have been first applied to 45 benchmark functions and have been evaluated and analyzed statistically. Then they have been applied to six classic engineering problems. Algorithms presented in each step have been compared with Genetic Algorithm (GA) and PSO algorithms and their combined approaches. The results of experiments and statistical analysis show that the solution to engineering problems has been done more effectively by using the two proposed combinations.

A hybrid algorithm for the dimensional synthesis of parallel manipulators

Increasing the number of non-dominated solutions among multiple objectives is beneficial for determining the optimal comprehensive performance of parallel manipulators (PMs). In engineering, we often achieve this by increasing the population size of intelligent optimization algorithms, which leads to an exponential increase in computational costs, especially for high computationally intensive objective functions. How to increase the number of non-dominated solutions without significantly increasing computational costs is a challenge faced by the dimensional synthesis of PMs. A hybrid algorithm combing particle swarm optimization (PSO) and direct search algorithms was proposed in this work to fix this issue. And, a procedure for direct search algorithm to determine the Pareto optimal front is proposed. The proposed hybrid algorithm is implemented in three steps: (1) The design parameter space is coarsely meshed to determine the amplitude of objective functions to achieve its dimensionless purpose; (2) the PSO algorithm is adopted to obtain the approximate Pareto front, as well as the Pareto front hypersurface stretched by the optimal design parameter space; (3) the neighborhood of the approximate Pareto optimal set was finely meshed, and the direct search algorithm is proposed to update the non-dominated solutions that are closer to the real Pareto front. The proposed hybrid algorithm can obtain more non-dominated solutions while avoiding the significant increase in computational costs caused by increasing population size. The proposed algorithm was implemented using the dimensional synthesis of the 2UPR-RPU PM. Compared with the PSO algorithm, which produced 80 sets of non-dominated solutions, the proposed hybrid algorithm generated 242 sets of solutions with the computational costs only increased 32%.

Efficient Energy Management System for AC–DC Microgrid and Electric Vehicles Utilizing Renewable Energy With HCO Approach

ABSTRACTThe reliability of various energy sources can be increased and distributed production and renewable energy can be fully integrated into the power grid on a wide scale through the growth and development of the microgrid (MG). Global energy difficulties are brought about by the finite supply of fossil fuels and the world's expanding energy consumption. Due to these challenges, the electric power system has to convert to a renewable energy‐based power generation system to produce clean, green energy. However, because of the unpredictable nature of the environment, the shift toward the use of renewable energy sources raises uncertainty in the production, control, and power system operation. This manuscript proposes a renewable energy‐based energy management system for electric vehicles and AC–DC MGs. The proposed method is Hermit Crab Optimizer (HCO). The major goal of the proposed strategy is to supply steady power regardless of generation disparity, which should stop the storage devices from degrading too quickly. The HCO approach provides a stable power balance for MG operation. The proposed technique efficiently strikes a power balance to meet load requirements and recharge electric cars. By then, the proposed strategy is implemented in the MATLAB platform and the execution is computed with the existing procedure. The proposed technique displays better outcomes in all existing systems like biogeography‐based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial neural network (ANN). The existing technique shows the cost of 25$, 30$, 35$, 40$, and the proposed technique displays the cost of 20$ which is lower than the other existing techniques.

Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm

Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it is important for optimizing field management measures and improving crop productivity. To accurately and quickly predict citrus yield, this study obtained multispectral images of citrus fruit maturity through an unmanned aerial vehicle (UAV) and extracted multispectral vegetation indices (VIs) and texture features (T) from the images as feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), and multiple stepwise regression (MSR) models were used to construct citrus fruit number and quality prediction models. The results show that, for fruit number prediction, the XGB model performed best under the combined input of VIs and T, with an R2 = 0.792 and an RMSE = 462 fruits. However, for fruit quality prediction, the RF model performed best when only the VIs were used, with an R2 = 0.787 and an RMSE = 20.0 kg. Although the model accuracy was acceptable, the number of input feature variables used was large. To further improve the model prediction performance, we explored a method that utilizes a hybrid coding particle swarm optimization algorithm (CPSO) coupled with XGB and SVM models. The coupled models had a significant improvement in predicting the number and quality of citrus fruits, especially the model of CPSO coupled with XGB (CPSO-XGB). The CPSO-XGB model had fewer input features and higher accuracy, with an R2 > 0.85. Finally, the Shapley additive explanations (SHAP) method was used to reveal the importance of the normalized difference chlorophyll index (NDCI) and the red band mean feature (MEA_R) when constructing the prediction model. The results of this study provide an application reference and a theoretical basis for the research on UAV remote sensing in relation to citrus yield.

A risk assessment method for power internet of things information security based on multi‐objective hierarchical optimisation

AbstractPower Internet of Things (PIoT) relies on various smart sensors and edge devices to support the operational status of the power grid. To address the challenge of conducting a comprehensive information security risk assessment for communication among heterogeneous devices in PIoT, a reliable security risk assessment method is proposed. This method, based on the analytic hierarchy process (AHP), considers four key dimensions: the cloud platform, communication transmission, edge side and terminal side. It integrates information indexes, the asset loss and risk probability to construct a risk assessment scheme oriented towards PIoT information security. The judgement matrix of AHP which fails the consistency test, is modified by using an improved multi‐objective particle swarm optimisation (IMPSO) algorithm, while minimising semantic offset distance. Verified through a case study, this method effectively reduces the consistency index to an average of 0.01 and achieves a correlation of 90.86% with traditional AHP assessment results.