Combining Particle Swarm Optimization Research Articles

A High-Precision Car-Following Model with Automatic Parameter Optimization and Cross-Dataset Adaptability

Despite the significant impact of network hyperparameters on deep learning car-following models, there has been relatively little research on network hyperparameters of deep learning car-following models. Therefore, this study proposes a car-following model that combines particle swarm optimization (PSO) and gated recurrent unit (GRU) networks. The PSO-GRU car-following model is trained and tested using data from the natural driving database. The results demonstrate that compared to the intelligent driver model (IDM) and the GRU car-following model, the PSO-GRU car-following model reduces the mean squared error (MSE) for the speed simulation of following vehicles by 88.36% and 72.92%, respectively, and reduces the mean absolute percentage error (MAPE) by 64.81% and 50.14%, respectively, indicating a higher prediction accuracy. Dataset 3 from the drone video trajectory database of Southeast University and NGSIM’s I-80 dataset are used to study the car-following model’s cross-dataset adaptability, that is, to verify its transferability. Compared to the GRU car-following model, the PSO-GRU car-following model reduces the standard deviation of the test results by 60.64% and 32.89%, highlighting its more robust prediction stability and better transferability. Verifying the ability of the car-following model to produce the stop-and-go phenomenon can evaluate its transferability more comprehensively. The PSO-GRU car-following model outperforms the GRU car-following model in creating stop-and-go sensations through platoon simulation tests, demonstrating its superior transferability. Therefore, the proposed PSO-GRU car-following model has higher prediction accuracy and cross-dataset adaptability compared to other car-following models.

Optimized machine learning enabled intrusion detection 2 system for internet of medical things

This research paper proposes an intrusion detection system that combines particle swarm optimization and AdaBoost algorithms to classify and detect malware-related records in innovative health app platforms. The research employs the NSL KDD dataset, consisting of 125,973 instances and 41 features, with data split into training (20 %) and testing (80 %) sets. A feature selection process using Particle Swarm Optimization identifies 12 relevant features for intrusion detection. The intrusion detection system effectively categorizes various attack types, including Denial of Service (DoS), User-to-root (U2R), Root-to-local (R2L), and Probe attacks. Regarding classifier performance, AdaBoost exhibits the highest recall value (0.966667), highlighting its strong intrusion detection capabilities. The experimental results demonstrate that the PSO-AdaBoost approach achieves superior accuracy, precision, and recall in intrusion detection. By integrating machine learning-based intrusion detection systems into smart health apps, healthcare providers can enhance patient care, reduce costs, and ensure the confidentiality of sensitive medical information. This research highlights the potential of ML-IDSs in strengthening the security infrastructure surrounding medical IoT devices and improving patient outcomes in the interconnected world of the Internet of Medical Things.

A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud–fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA.The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes.

Fractional crop-planting area projection by integrating geographic grid data and agricultural statistics based on random forest regression

ABSTRACTAccurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative characteristics. We present a robust method to disaggregate the agricultural statistics within each county unit to 1-km scale grid by combining particle swarm optimization (PSO)-based feature selection with Random Forest (RF) regression, and an iterative area-gapallocation(IAGA) method is implemented to reconcile the discrepancies between the disaggregating results and statistics. The agriculture in Gansu Province, China, is characterized by complex heterogeneous smallholder farming landscapes. We tested this methodology in Gansu and explored the synergistic estimation of FCPA for six types of crops (i.e. wheat, maize, oil-bearing, vegetable,orchards, and other crops) in 2010. The results showed that the derived FCPA maps matched well with the statistics in terms of quantity, while also providing spatial details. The quantitative evaluation results indicated that the derived FCPA had good accuracy,with a higher R2 above 0.97, a lower RMSE below 1%, and an absolute error between 1.53-5.24%. The proposed methodology provides valuable insights for practical large-scale FCPA mapping at a high spatial resolution in a cost-effective manner.

Hybrid Framework for Enhanced Dynamic Optimization of Intelligent Completion Design in Multilateral Wells with Multiple Types of Flow Control Devices

Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.

Airborne Radio-Echo Sounding Data Denoising Using Particle Swarm Optimization and Multivariate Variational Mode Decomposition

Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational mode decomposition (VMD) algorithm. It processes data encompassing multiple channels. Determining the most effective component combination of the penalty parameter (α) and the number of intrinsic mode functions (IMFs) (K) is fundamental and affects the decomposition results. α and K in traditional MVMD are provided by subjective experience. We integrated the particle swarm optimization (PSO) algorithm to iteratively optimize these parameters—specifically, α and K—with high precision. This was then combined with the four quantitative parameters: energy entropy, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and root-mean-square error (RMSE). The RES signal decomposition results were judged, and the most effective component combination for noise suppression was selected. We processed the airborne RES data from the East Antarctic ice sheet using the combined PSO–MVMD method. The results confirmed the quality of the proposed method in attenuating the RES signal noise, enhancing the weak signal of the ice base, and improving the SNR. This combined PSO–MVMD method may help to enhance weak signals in deeper parts of ice sheets and may be an effective tool for RES data interpretation.

Single-channel speech enhancement by PSO-GSA with harmonic regeneration noise reduction

Speech quality significantly affects the performance of speech dependent systems. Noise in the background lowers the clarity and intelligibility of speech. The augmentation of speech can increase its quality. We propose a single-channel speech improvement framework that combines particle swarm optimization (PSO), gravitational search algorithm (GSA), and harmonic regeneration noise reduction (HRNR) to minimize speech signal noise and increase speech intelligibility. The proposed hybrid algorithm optimizes the amount of overlap between the noisy speech frames. This helps in reducing the overlapped noise. Then HRNR algorithm is applied to retain the speech harmonics. The algorithm gives improvement in the speech intelligibility for babble, car and exhibition noise. The segmental signal to noise ratio (SNR) is also improved for these noise types. There is improvement in speech intelligibility with minimal speech distortion.

HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets

The PSO-PARSIMONY methodology (a heuristic for finding accurate and low-complexity models with particle swarm optimization (PSO)) allows obtaining machine learning models with a good balance between accuracy and complexity. However, when the datasets are of high dimensionality, the methodology does not sufficiently reduce the complexity of the models. This paper presents a new hybrid methodology, called HYB-PARSIMONY, that combines PSO with genetic algorithm (GA) based methods. In the early stages of the optimization process, GA methods have a preponderance to accelerate the search for parsimony. Later, PSO becomes more relevant to improve accuracy. This new methodology obtains significant improvements in the search for more accurate and low-complexity models in high-dimensional datasets.

Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

In order to reduce power consumption and delay time when mobile devices use wireless networks, this paper proposes dividing groups of mobile devices into clusters, and proposes two cluster architectures, which are dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by 2-hop of priority weighted round robin (DRONEE-PFT2-PWRR) and dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by multi-hop of priority weighted rate control (DRONEE-PFTM-PWRC). Furthermore, our proposed DRONEE-PFT2-PWRR algorithm increases cluster coverage using 2-hop and our proposed DRONEE-PFTM-PWRC algorithm improves power consumption of the system by multi-hop cluster architecture. Internal cluster communication uses Wi-Fi transmission packets, which can reduce energy consumption when a mobile device communicates with a long term evolution advanced (LTE-A) base station, and reduce interference on signals between mobile devices. In the selection of cluster heads, this paper uses particle swarm optimization (PSO) to find the best cluster head position, and the PSO fitness parameter value is adjusted using fuzzy logic control (FLC) combined with the Taguchi method. Priority weighted round robin (PWRR) architecture is proposed for LTE-A base station and cluster network resource allocation, and quality of service is introduced to give priority weight to packets according to data type. Furthermore, priority weighted rate control (PWRC) is proposed. This method determines whether the network resources of the base station meet the demands of the cluster, and then assigns weights according to the requirements of the cluster and its priorities, and finally allocates network resources according to those weights. This increases the uplink throughput of network resource allocation. Simulation results show that our proposed methods significantly outperform the DRONEE-weighted (DRONEE-W) algorithm method in terms of power consumption, network throughput, and transmission delay time.

Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strategy.

Optimal dispatch of integrated energy system with P2G considering carbon trading and demand response.

How to maintain the economic and low-carbon operation of the integrated energy system (IES) while taking into account the interests of the user side is of great significance to promote the large-scale development of IES. For this reason, this paper takes IES with the electricity-to-gas device as the research object and first constructs a demand response model that takes into account the user's energy experience and a reward and punishment ladder carbon trading model. Second, an IES game optimization framework considering carbon trading and demand response is proposed. On this basis, taking the operators and users of the integrated energy system as the main players of the game, a two-level game optimization scheduling model is constructed to synchronously improve the economic, low-carbon, and satisfactory operation of IES, and the proposed model is solved by the combination of particle swarm optimization and CPLEX. Finally, the simulation shows that the proposed model can effectively enhance the benefits of both supply and demand while reflecting the user response behavior more realistically.

Particle swarm optimization‐XGBoost‐based modeling of radio‐frequency power amplifier under different temperatures

AbstractXGBoost is the optimization of gradient boosting with the best overall performance among machine learning algorithms. By introducing a regularization term into the loss function of gradient boosting, XGBoost can effectively limit the complexity of the model, improve the generalization ability, and solve the overfitting problem. In this paper, XGBoost is first introduced into modeling radio‐frequency (RF) power amplifiers (PA) under different temperatures. Furthermore, the modeling effect of XGBoost is mainly dependent on hyperparameters. As traditional grid search is time‐consuming and labor‐intensive, this paper combines particle swarm optimization (PSO) with XGBoost for searching hyperparameters. The experimental results show that XGBoost can effectively suppress the overfitting problem in gradient boosting while modeling RF PAs in different ambient temperatures. In addition, compared to classic machine learning algorithms, including support vector regression (SVR), gradient boosting, and XGBoost, the proposed PSO‐XGBoost can increase the modeling accuracy by one order of magnitude or more while also increasing the modeling speed by more than one magnitude or more. The PSO‐XGBoost model proposed in this paper can be introduced into modeling other microwave/RF devices and circuits to improve modeling accuracy and reduce modeling time.

A novel hybrid optimization framework for sizing renewable energy systems integrated with energy storage systems with solar photovoltaics, wind, battery and electrolyzer-fuel cell

This study proposes a novel approach to evaluate the integration of solar photovoltaic (PV) and wind turbine renewable energy systems (RES) with Electrolyzer-Fuel Cell Energy Storage System (EFCS) and Battery Energy Storage System (BESS). The objective is to minimize the weighted average cost of energy (waCOE) while maintaining a specific renewable energy fraction (FRES). To achieve this objective, a new hybrid optimization system that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to simultaneously optimize the capacity of PV, wind turbine, battery, electrolyzer, hydrogen tank, and fuel cell, which makes it a complex and nonlinear optimization problem. The proposed method produces more practical outcomes by implementing some constraints on PSO and strengthens its ability to exploit optimization opportunities. Six scenarios representing different integrated systems are investigated and compared technically and economically, including PV + BESS, PV + EFCS, Wind + BESS, Wind + EFCS, PV + Wind + BESS, and PV + Wind + EFCS. A case study is conducted on a university campus situated on a Mediterranean island, where electricity production relies on imported fossil fuel. The findings indicate that a PV-wind hybrid system consisting of a 1.15 MW wind turbine, a 2.89 MW PV, and a 2.31 MWh BESS has the lowest waCOE of 0.1838 €/kWh. This system is capable of providing a demand–supply fraction (DSF) of 44.63% with a FRES of 60.1%. The study found that a hybrid system consisting of PV and wind turbines showed promise in the examined region due to their complementary characteristics. Additionally, combining various RES with BESS with approximately 33–35% lower waCOE was found to be more economically viable and resulted in 16–20% higher DSF compared to EFCS.

Bi-level and multi-objective optimization of renewable energy sources and storage planning to support existing overloaded electricity grids

The paper proposes a bi-level multi-objective optimization model to optimally design and operate renewable energy sources and storage systems in an existing electrical grid with increasing demand. while respecting network constraints based. The bi-level optimization model combines particle swarm optimization (PSO) and dynamic linear AC-optimal power flow (DLOPF). The aim of the bi-objective model is to minimize costs while limiting carbon emissions. PSO sizes and identifies the placement of battery energy storage (BES) systems, and DLOPF locates and sizes RES in a spatial–temporal framework using the Levelized Cost of Energy and Storage. Different scenarios were applied to the IEEE-30 bus system to reveal the behavior of the dynamic network for the different cases, and the associated effect of integrating RES and BES. The model resulted in optimized scenarios in terms of placement and sizing of RES and BES at the lowest cost while considering cost minimization and carbon constraints. This shows how the complexity of the CO2 constraints requires more RES and BES installations and thus more funds. The maximum CO2 limit achieved is 30%, which has reached the storage limits and increased the overall cost by $15 m.

Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 Ki, 0.7741 Kp, and 0.1850 Kd. The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network.

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining Particle Swarm Optimization and Efficient Genetic Algorithm

Wireless Sensor Networks (WSNs) have become a crucial component of numerous applications, including the military, healthcare, and environmental monitoring. A promising approach to increasing the lifespan of the sensor network is cluster-based WSNs. In WSNs, choosing the best cluster head is a crucial task that has an impact on the network's performance and energy efficiency. There are various issues with current methods for choosing the cluster head, including nodes dying too soon, uneven energy usage, and shorter network lifetimes. Moreover, traditional methods such as Randomized Clustering and Fixed Cluster Head are not effective in prolonging the network lifetime as they do not consider the energy consumption and residual energy of nodes. In this paper, an optimal selection of cluster head is presented where we combine the Particle Swarm Optimization (PSO) and Efficient Genetic Algorithm (EGA). Firstly, PSO is used to randomly select the cluster head and update the position of each cluster. Thereafter, EGA invokes its fitness values to select the best cluster head that transmits information to the base station. The simulation result shows that the performance improvement of the proposed method PSO_EGA in terms of network lifetime is 0.10% against Improve Cuckoo Search Algorithm (ICSA) and 0.20% against Hybrid Crow Search Algorithm (HCSA), packet to cluster head is 7% against ICSA and 16% against HCSA, packet to sink is 11% against ICSA and 22% against HCSA and number of alive node is 28% against ICSA and 48% against HCSA. Therefore, our proposed method outperforms ICSA and HCSA in terms of the aforementioned parameters.

Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project.

A robust identification procedure for phase field fracture mechanics parameters

The classical Phase Field (PF) model for fracture mechanics of brittle materials based on the finite element method involves three parameters in addition to the Poisson ratio ν: the Young’s modulus E, the fracture toughness Gc, and the internal length scale lc. The latter is mathematically conceived as a numerical regularization parameter that should tend to zero to recover linear elastic fracture mechanics (LEFM) predictions. To address this issue, a robust algorithm is implemented in MATLAB, which combines Particle Swarm Optimization (PSO) and the Phase Field (PF) approach to fracture based on the finite element method. The algorithm has been applied to a series of uni-axial tensile tests (with E and lc to be identified) and to three-point bending tests (with E, lc and also Gc to be identified) on specimens made of ABS material. Results show that the optimal values of E and Gc are consistent in both tests, while lc presents a significant dependency upon the test type. Therefore, different values of the internal length scale should be identified and used to match the experimental responses under uni-axial tension or bending.

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.