Particle Swarm Research Articles

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.

An Inventory Model of a Decaying Item with Fuzzy Lifetime and Decay Rate under Dynamic Trade Credit and Variable Demand

An inventory model of a decaying item is proposed considering dynamic credit period to the consumers, fuzzy life time of the units, time-dependent fuzzy decaying rate, and freshness dependent dynamic imprecise demand. Decaying rate of units depends on its lifetime and is considered as a dynamic monotonic increasing function of the same. To encourage the consumers, the retailer provides a credit on a part of the purchase and due to the dynamic decreasing nature of the freshness of units, the credit interval is considered as dynamic increasing. The unit cost, consumer credit period, and credit amount have impact on demand. As demand is fuzzy, some units may left at any cycle ending and are put up for sell in lot in a lump sum price. The model is formulated using fuzzy mathematical tools to decide the optimal cycle length, unit selling price, and order quantity to optimize the annual fuzzy profit from a cycle. A modified form of particle swarm optimization is applied to get the best decision, in which, using fuzzy credibility measure the objectives corresponding to two feasible solutions are compared to select the better one. Proper fuzzy simulation approaches are followed to determine the required credibility measures.

Improved dynamic programming method for solving multi-objective and multi-stage decision-making problems

Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm. The proposed algorithm has been verified on 12 benchmark test functions and a multi-objective travelling salesman problem. The results demonstrate that the NSDP algorithm achieves better outcome in multiple performance metrics and higher solving efficiency, compared with non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization.

Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization

Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization

An optimized LSTM-based deep learning model for anomaly network intrusion detection

The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. Nevertheless, most previously recommended IDSs exhibit a notable false alarm rate. To mitigate this challenge, exploring deep learning methodologies emerges as a viable solution, leveraging their demonstrated efficacy across various domains. Hence, this article proposes an optimized Long Short-Term Memory (LSTM) for identifying anomalies in network traffic. The presented model uses three optimization methods, i.e., Particle Swarm Optimization (PSO), JAYA, and Salp Swarm Algorithm (SSA), to optimize the hyperparameters of LSTM. In this study, NSL KDD, CICIDS, and BoT-IoT datasets are taken into consideration. To evaluate the efficacy of the proposed model, several indicators of performance like Accuracy, Precision, Recall, F-score, True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic curve (ROC) have been chosen. A comparative analysis of PSO-LSTMIDS, JAYA-LSTMIDS, and SSA-LSTMIDS is conducted. The simulation results demonstrate that SSA-LSTMIDS surpasses all the models examined in this study across all three datasets.

Modified COST-235 Empirical Pathloss Model for Agricultural WSN Using Particle Swarm Optimization

Modified COST-235 Empirical Pathloss Model for Agricultural WSN Using Particle Swarm Optimization

Multi-objective Optimization of a Yoke-less Axial Flux Switching PM Motor based on Hybrid Particle Swarm Optimization

In this study, a hybrid particle swarm optimization (HPSO) approach is presented for enhancing the performance of a three-phase, 12-slot, 19-pole yoke-less axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. The aim of the optimization process is to achieve a motor that demonstrates high efficiency and stable operation. To facilitate this, a finite element analysis model is developed, and a multi-objective optimization function utilizing weight coefficients is created. The variables in this study include the split ratio, stator axial length, sandwiching pole angle, rotor pole angle, permanent magnet arc, and the number of conductors per slot. The optimization objectives encompass efficiency, power factor, cogging torque, and average torque, evaluated under both no-load and load conditions. Findings indicate that the hybrid particle swarm optimization method excels in its search capabilities and convergence speed, resulting in motor designs that align closely with the intended specifications. Finite element simulations further validate the proposed methodology's effectiveness and practicality. Finally, experimental results are presented to confirm the reliability of the suggested algorithm.

Heat Transfer Study of a Ternary Hybrid Nanofluid Flow in a Rotating Horizontal Annulus Using Artificial Intelligence Computing Techniques: Application of Particle Swarm Optimization and Artificial Neural Network

Heat Transfer Study of a Ternary Hybrid Nanofluid Flow in a Rotating Horizontal Annulus Using Artificial Intelligence Computing Techniques: Application of Particle Swarm Optimization and Artificial Neural Network

A Hybrid Honey Badger Algorithm to Solve Energy-Efficient Hybrid Flow Shop Scheduling Problems

A well-planned schedule is essential to any organization’s growth. Thus, it is important for the literature to cover a more comprehensive range of scheduling problems. In this paper, energy-efficient hybrid flow shop (EEHFS) scheduling problems are considered. Researchers have developed several techniques to deal with EEHFS scheduling problems. Also, researchers have recently proposed several metaheuristics. Honey Badger Algorithm (HBA) is one of the most recent algorithms proposed to solve various optimization problems. The objective of the present work is to solve EEHFS scheduling problems using the Hybrid Honey Badger Algorithm (HHBA) to reduce the makespan (Cmax) and total energy cost (TEC). In the HHBA, a constructive heuristic known as the NEH heuristic was incorporated with the Honey Badger Algorithm. The suggested algorithm’s performance was verified using an actual industrial scheduling problem. The company’s results are compared with those of the HHBA. The HHBA could potentially result in an 8% decrease in total energy cost. Then, the proposed algorithm was applied to solve 54 random benchmark problems. The results of the proposed HHBA were compared with the FIFO dispatching rule, the NEH heuristic, and other metaheuristics such as the simulated annealing (SA) algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, Honey Badger Algorithm, and the Ant Colony Optimization (ACO) algorithms addressed in the literature. Average percentage deviation (APD) was the performance measure used to compare different algorithms. The APD of the proposed HHBA was zero. This indicates that the proposed HHBA is more effective in solving EEHFS scheduling problems.

Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost

Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost

LEA-RPL: lightweight energy-aware RPL protocol for internet of things based on particle swarm optimization

This paper addresses the issue of quality of service routing optimization within the Internet of Things networks. We particularly focus on the energy-aware and the lightweight aspects. By recognizing the relationship between lightweight and energy-aware routing, we set out to study their combined benefits. This study aims to enhance the Routing Protocol for Low Power and Lossy Networks by integrating energy-awareness and lightweight characteristics based on the Particle Swarm Optimization algorithm. Our approach addresses energy consumption, routing overhead and decision complexity in route establishment. The principal contributions include the introduction of an objective function that considers Expected Life Time, Delay and a new proposed metric Energy Aware-Expected Transmission Count. The improvement of the Long Short Term Memory predicting inertia weight based PSO with Online Gradient Descent that is used to optimize both the parent selection process and Trickle Timer mechanism. The controlled parent switching process to solve unnecessary and frequent changes. Our approach is validated through simulations in Contiki COOJA, with thorough comparisons with some existing protocols based on packet delivery ratio, average energy consumption, convergence time, control overhead, average end-to-end delay and average parent switching as performance metrics. The results reveal that our approach performs better. Depending on the protocol used for comparison, our approach reduced parent switching by 42.59–61.73%, convergence time by 20.31–66.06%, control overhead by 14.4–23.64%, energy consumption by 29.86–49.6%, end-to-end delay by 7.66–40.81% and increased packet delivery ratio by 2–42.92%.

Advanced control parameter optimization in DC motors and liquid level systems

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles. This paper introduces a novel approach employing MGO to optimize control parameters in both a DC motor and three-tank liquid level systems. The fine-tuning of proportional-integral-derivative (PID) controller parameters using MGO achieves remarkable results, including a rise time of 0.0478 s, zero overshoot, and a settling time of 0.0841 s for the DC motor system. Similarly, the liquid level system demonstrates improved control with a rise time of 11.0424 s and a settling time of 60.6037 s. Comparative assessments with competitive algorithms, such as the grey wolf optimizer and particle swarm optimization, reveal MGO’s superior performance. Furthermore, a new performance indicator, ZLG, is introduced to comprehensively evaluate control quality. The MGO-based approach consistently achieves lower ZLG values, showcasing its adaptability and robustness in dynamic system control and parameter optimization. By providing a dependable and efficient optimization methodology, this research contributes to advancing control systems, promoting stability, and enhancing efficiency across diverse industrial applications.

Rapid identification of Liubao tea vintage based on terahertz spectroscopy combined with improved differentiated creative search algorithm

As a special microbial fermented tea, the aging year of Liubao tea is a crucial determinant of its value. This study established a fast and high-precision method for identifying the age of Liubao tea by combining terahertz time-domain spectroscopy technology with chemometric methods. Most common optimization algorithms rely too much on the guidance of elite individuals in the optimization process and are prone to fall into local optimal solutions. Therefore, this paper uses a differentiated creative search algorithm with global thinking to optimize the support vector machine model parameters. To address the problem of poor search results due to unclear goals in the algorithm’s convergence and divergence processes, a guided learning strategy is employed to balance these schemes within the differentiated creative search algorithm. This approach yields a classification model with higher search efficiency. Compared with the support vector machine models optimized by Genetic Algorithm, Particle Swarm Optimization, and differentiated creative search algorithm, the new model achieved the best classification performance, with an accuracy of 96.87% and an F1 score of 0.9683. The results indicate that the guided learning strategy can balance the updating scheme of the differentiated creative search algorithm. The optimized model enables accurate qualitative analysis of the year of Liubao tea, offering a feasible solution for applying terahertz spectroscopy technology in tea identification.

Dynamic coupling of qualitative–quantitative models for operation of water resources based on environmental criteria

Concerning issues include the distribution of scarce water resources, the quality of utilized water, environmental repercussions, and regulations for the sustainable use of water resources. In the management of water resources, optimal qualitative–quantitative exploitation of surface water bodies is regarded as a desirable strategy. The Dez River surface water system from the Dez regulatory dam to Band-e-Ghir is selected in the current paper to create a qualitative–quantitative model that can determine the best exploitation strategies. A dynamic linkage between qualitative and quantitative models is built in order to simulate the current exploitation conditions under the umbrella of the best-case scenario. In this coupled system, hydraulic relationships are established between all of the system’s components. The available data are shared between two models in this structure to simulate the qualitative and quantitative effects of surface water. Then, a new structure is produced to derive the best policies for exploiting the dam and the river by connecting the multi-objective particle swarm optimization algorithm with the qualitative–quantitative coupled model body. The monthly river environmental demand is one of the decision variables in the ideal scenario, and the goals include boosting the percentage of supply demands and minimizing the violation of quality standards. The best-case scenario’s implementation increases the likelihood that all plain demands will be met, regardless of priority. Furthermore, in comparison with the reference scenario, the results of the optimal scenario show that not only are the concentrations of contaminants and qualitative parameters increased, but there are also only minimal violations of the quality and pollution standards of the river water in the majority of river points, particularly in the locations of agricultural withdrawals. The findings demonstrate that using the qualitative–quantitative dynamic relationship between water resources and the development of the coupled model using the NSGA-II algorithm allows us to better plan for the appropriate use of existing water resources by taking into account all stakeholders in such a way that, in addition to meeting needs, maintains the river quality close to standard limits throughout the exploitation period. By using this strategy, users will be informed of the negative effects of their actions, as well as the encroachment on river boundaries and associated consequences.

Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization

Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach manipulates the solutions obtained by both algorithms in a different way. The objective of this combination is to overcome the GWO stuck-in-local-optima problem that might occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. Both S-shaped and V-shaped binary transfer functions were used to convert the continuous solutions obtained from each proposed approach to their corresponding binary versions. The three proposed approaches were evaluated using nine small-instance, high-dimensional, cancer-related human gene expression datasets. A set of comparisons were made against the original binary versions of both GWO and PSO algorithms and against eight state-of-the-art feature selection binary optimizers in addition to one of the recent binary optimizers that combines PSO with GWO. The evaluation results showed that one of the proposed S-shaped and V-shaped approaches achieved 0.9 and 0.95 average classification accuracy, respectively, while selecting the fewest number of features. The results also confirmed the superiority of one of the proposed V-shaped approaches when compared with the original binary GWO and PSO approaches. Moreover, the results confirmed the superiority, in most of the datasets, of one of the three approaches over the state-of-the-art approaches. Finally, the results revealed that the best approach in terms of classification accuracy, fitness value, and number of selected features had the highest computational complexity.

Machine Learning-Based Lithium Battery State of Health Prediction Research

To address the problem of predicting the state of health (SOH) of lithium-ion batteries, this study develops three models optimized using the particle swarm optimization (PSO) algorithm, including the long short-term memory (LSTM) network, convolutional neural network (CNN), and support vector regression (SVR), for accurate SOH estimation. Key features were extracted by analyzing the temperature, voltage, and current curves of the battery, and health factors with high correlation to SOH were selected as model inputs using the Pearson correlation coefficient. The PSO algorithm was employed to optimize model parameters, resulting in the construction of three predictive models: PSO-LSTM, PSO-CNN, and PSO-SVR. The models were validated using the NASA PCoE battery aging datasets B0005, B0006, and B0007, with prediction accuracy evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2). Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R2 exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. Among the three models, PSO-LSTM exhibited the best predictive performance, achieving a minimum MAE of 0.67%, RMSE of 0.94%, MAPE of 45.82%, and R2 as high as 0.9298 across the three datasets. These findings suggest that the PSO-LSTM model provides a robust reference for accurate SOH prediction of lithium-ion batteries and shows promising potential for practical applications.

Interval-valued optimal control strategies for vaccine distribution and infection awareness investment in preservation technology

Purpose This study aims to explore the global threat of diseases that affect people, such as diarrheal, Hepatitis B, Rotavirus, Measles diseases, emphasizing the integration of disease vaccines into immunization programs globally as recommended by the World Health Organization, resulting in significant case reductions. Design/methodology/approach Notably, it stresses the necessity of raising awareness about diseases and vaccines through promotional efforts alongside effective inventory management because of vaccine perishability, highlighting preservation techniques and cold storage. Addressing environmental concerns, including carbon emissions from vaccine deterioration, the study proposes green technology investments aligned with Sustainable Development Goals to mitigate these impacts. Additionally, advanced optimization algorithms, including ant colony, modified flower pollination, cuckoo search and particle swarm optimization algorithms, are used to optimize pricing, preservation strategies, green investments and replenishment schedules. The research also uses the concept of interval values to enhance the robustness of the optimization framework. Through numerical experiments, the study demonstrates the effectiveness of this dynamic investment approach, providing empirical validation. Findings Furthermore, sensitivity analysis on critical parameters yields valuable insights for decision-makers, underscoring the importance of dynamically managing vaccine inventory. The study offers practical solutions and managerial insights that can inform policy decisions and strategic planning in disease response efforts. Originality/value This study concludes by emphasizing how creative green technology approaches can help decision-makers manage the social and environmental effects of vaccine inventories in the health care of people.

Research on design and control method of active vibration isolation system based on piezoelectric Stewart platform.

Space payloads in orbit are vulnerable to small vibrations from satellite platforms, which can degrade their performance. Traditional methods typically involve installing a passive vibration isolation system between the platform and the payload. However, such systems are usually effective only for high-frequency, large-amplitude vibrations and perform poorly in isolating low-frequency vibrations and resonances below 10Hz. To address this limitation, this paper proposes an active vibration isolation system using a 6-degrees-of-freedom Stewart platform driven by piezoelectric actuators. First, the characteristics of the Stewart platform are analyzed and modeled, with the deformation displacement of each leg calculated through decoupling, allowing for high-precision servo control. Next, given the inherent hysteretic nonlinearity of piezoelectric ceramics, which significantly affects positioning accuracy, the hysteresis mechanism of the actuators is analyzed, and a phenomenological mathematical model based on Bouc-Wen operators is established. A Modified particle swarm optimization (MPSO) method is proposed for identifying the model's nonlinear parameters, significantly enhancing the optimization efficiency. Finally, feedforward inverse compensation and feedback linearization methods are introduced. Experimental results verify that the designed active-passive vibration isolation system greatly improves both the positioning accuracy of the piezoelectric actuators and the active vibration isolation performance of the platform.

Optimization analysis of the performance of a ground source heat pump system based on air conditioning load forecasting

Optimization analysis of the performance of a ground source heat pump system based on air conditioning load forecasting

Enhancing Underwater Object Recognition: Integrating Transfer Learning with Hybrid Optimization Techniques for Improved Detection Accuracy

Underwater object recognition presents unique challenges due to varying water conditions, low visibility, and the presence of noise. This research proposes an advanced methodology that combines transfer learning and hybrid optimization techniques to enhance recognition accuracy in underwater environments. Specifically, a pre-trained EfficientNet model is employed for feature extraction, leveraging its capacity to capture diverse features in underwater images. The model is then optimized using a hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA) to fine-tune hyperparameters such as learning rate, number of layers, and activation functions. This hybrid approach balances exploration and exploitation in the search space, allowing the model to converge on an optimal solution that maximizes accuracy. The model is evaluated against nine existing deep learning models, including ResNet-50, VGG-16, EfficientNet-B0, and MobileNetV2. The proposed PSOGA model achieves a superior accuracy of 98.32%, surpassing the best-performing models like EfficientNet-B0, which reached 95.89%. Furthermore, the model outperforms traditional optimizers like Adam, RMSprop, and AdaGrad, which attained lower accuracies. Precision, recall, and F1-score for the PSOGA model also demonstrate remarkable improvements, highlighting the model's effectiveness in underwater object recognition. The combination of transfer learning and hybrid optimization enables the model to generalize well across diverse underwater environments while maintaining computational efficiency.