Particle Swarm Optimization Method Research Articles

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods

Skin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and different feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%.

Optimizing dynamic measurement accuracy for machine tools and industrial robots with unscented Kalman filter and particle swarm optimization methods

The telescoping ballbar is widely utilized for diagnosing accuracy and identifying faults in machine tools and industrial robots. Currently, there are no established standards for determining the optimal feed rate for ballbar tests. This lack of clear guidelines results in time inefficiency in measurements and inconsistencies in dynamic measurements, which complicates the comparison of ballbar test results under various conditions or across different machine platforms. To mitigate dynamic variations in ballbar results, an updated ballbar data processing method that integrates the unscented Kalman filter (UKF) and particle swarm optimization (PSO) was developed and validated using real ballbar data measured at multiple feed rates and simulated data with varying vibration magnitudes generated through the Renishaw ballbar simulator. Experimental results revealed that the dynamic components extracted from the ballbar results were observed to increase in correlation with the vibration measured at different feed rates and from the simulations. Moreover, the variations in the results measured at different feed rates after PSO-UKF processing were significantly reduced. The findings confirm the effectiveness of the proposed method in minimizing the dynamics of the ballbar results. Ultimately, this approach enhances the efficiency and accuracy of ballbar testing and offers a general method for improved diagnostics.

Automatic calibration of a discrete element model of a masonry arch by swarm intelligence methods

This paper addresses the automatic calibration of constitutive parameters of the Discrete Element Method (DEM) models of masonry structures. Three swarm intelligence algorithms were applied, which are efficient in problems characterised by the small number of unknown parameters but high computational demand for the evaluation of the target function. Trust-Based Particle Swarm Optimisation (TBPSO) was chosen due to its excellent convergence rate. It was already tested that TBPSO converged faster than the original Genetic Algorithm and Particle Swarm Optimisation Methods. Therefore, TBPSO was compared with two recent and promising methods, the Marine Predator Algorithm (MPA) and the Golden Eagle Optimiser (GEO). The paper also presents a novel two-step approach, which enhances the performance of the tested optimisation strategies. The methodology is capable to account for and precisely find the mode of failure using Convolutional Neural Networks (CNN). The three optimisation methods and the two-step approach were applied on the DEM model of a quasi-static masonry arch collapse experiment from the literature. Performance of the three algorithms in identifying the appropriate constitutive parameters values of the DEM model were compared. Clear evidence on the advantage of the two-step approach with TBPSO is demonstrated. Remarks about different modelling issues are also included.

Multi-time-scale voltage control of the distribution network with energy storage equipped soft open points

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance.

Study of biocapacity areas to reduce ecological footprint deficits: A case study of Turkey

Our world has had difficulty meeting humans' needs in recent years. To ensure that the world can sustain its inhabitability and self-sufficiency in terms of natural resources, it is required to make the total amount of biocapacity areas equal to or higher than the ecological footprint. An analytical study has been carried out to remedy the biocapacity deficit by utilizing this information for Turkey and then these areas are optimized with heuristic optimization techniques. As a result, Artificial Bee Colony provides better objective function results (fewer errors) compared to Particle Swarm Optimization and Global Optimization Method Based on Clustering and Parabolic Approximation in terms of minimum, maximum, average value, and standard deviation. The rates of change according to the current situation of the biocapacity areas in 2016 are 277.97 %, 30.28 %, −29.28 %, 14.97 %, and −44.85 % for cropland, grazing land, forestland, fishing grounds, and built-up land, respectively. Depending on the population growth, these rates should additionally change by 83.24 %, −0.69 %, 3.97 %, 6.22 %, and −14.24 % respectively in 2050. The developed model can be used in industry or within the frame of government development policy and thus the balance between ecological footprint and biocapacity can be kept under control.

Stability and Reactive Power Sharing Enhancement in Islanded Microgrid via Small-Signal Modeling and Optimal Virtual Impedance Control

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability.

Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

AbstractMixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.

Design and optimization of a two-degrees-of-freedom single-sided vibro-impact nonlinear energy sink for transient vibration suppression of a thin plate

To rapidly suppress the transient vibration response of a thin plate, a two-degrees-of-freedom single-sided vibro-impact nonlinear energy sink (TSSVI NES) is proposed in this paper. Based on the Kirchhoff hypothesis, the coupled equations of motion between the plate and the TSSVI NES are established. The system is modeled by the Galerkin technique and numerically solved using the fourth order Runge-Kutta method. To obtain the optimal parameters of the TSSVI NES, a particle swarm optimization method is adopted. The system response with the TSSVI NES is compared with that of the cubic nonlinear energy sink (CNES) and the single-sided vibro-impact nonlinear energy sink (SSVI NES), showing that the vibration suppression ability of the TSSVI NES is better. The mechanism of rapid response suppression of the TSSVI NES is demonstrated by way of simulations. A parametric study is also conducted to determine the effects of damping, the coefficient of restitution, mass, stiffness, and installation position of TSSVI NES on the efficacy of the vibration control device. The numerical results demonstrate that the performance of the TSSVI NES is robust to the damping, coefficient of restitution, and installation position, but sensitive to mass difference and stiffness difference. Finally, the effect of the TSSVI NES is examined when the plate is excited by seismic loads, demonstrating a good response suppression performance.

Enhancing accuracy in field mobile robot state estimation with GNSS and encoders

The enhancement of accuracy remains a significant concern in state estimation for field mobile robots. This paper introduces a novel sensor fusion method to refine the accuracy of state estimation, employing global navigation satellite systems (GNSS) and encoders. This technique integrates a nonlinear observer for estimating the state of nonlinear systems and an unscented Kalman filter (UKF) to ascertain the robot’s state variables using signals from GNSS and encoder-based position sensors. In the proposed method, the nonlinear observer updates sigma points through nonlinear transformation and the UKF optimizes state estimation. Parameters are optimized through the particle swarm optimization method with the objective of minimizing the root mean square error of the state. To demonstrate performance, numerical simulations compare the proposed method with well-known nonlinear estimation algorithms, illustrating its effectiveness in field mobile robot state estimation. The method is further validated through implementation on a real-world mobile robot platform.

Replacement of raw materials in the formulation of industrial products based on near-infrared spectroscopy and particle swarm optimization

The replacement of raw materials in formulations is a crucial but challenging issue in industrial production. The goal is to substitute the inadequate or costly raw materials or their combinations with the adequate or potentially adequate inventory but while still ensuring the consistency and stability of product quality. In the present study, near-infrared (NIR) technology is first used for rapid and non-destructive analysis and further comprehensive characterization of the chemical characteristics of different raw materials. Afterwards, the particle swarm optimization (PSO) method is modified for the processing of NIR data and formulation replacement. Under the conditions of optimal constraints, an integrated strategy for replacement and optimization of raw materials in formulation is fully established. This yields optimized results for replacements that meet real demands in formulation design. In addition, the simulation of NIR spectra is initially realized by combining multiple Gaussian functions, which constructs simulated datasets that are suitable for verifying the accuracy and reliability of the proposed method. The real example of the replacement of raw materials is applied to comprehensively demonstrate the effectiveness and capability of the strategies proposed in this study. Among the 3,000 combinations used for formulation replacement, the recommended results obtained through the PSO method exhibit a high targeted proportion, attaining 81.82%, in which the difference between the optimal and original formulation is less than 0.0005. The results of expert validation indicate that 83% of the evaluations are consistent with spectral similarity assessments, which shows that the replaced results effectively meet the requirements for objective, scientific and digitized requirements in product manufacturing. Furthermore, this proposed method is applicable to various analysis and application scenarios on the basis of NIR analysis and global optimization.

Structural evolution and hydrogen storage properties of ScHn− (n = 10–20) clusters

An unbiased Crystal structure AnaLYsis by Particle Swarm Optimization (CALYPSO) method combined with density functional theory (DFT) study was adopted to identify the ground state ScHn− (n = 10–20) isomers. We have performed a series of investigation of electronic properties, structural evolution, hydrogen storage properties of ScHn− (n = 10–20) clusters. Using time-dependent (TD-)DFT, the photoelectron spectra (PES) of the low-lying structures of ScHn− clusters have been simulated. adsorption energies of ScHn− (n = 10–20) clusters are found in 2.47 eV-2.65 eV, and all hydrogen atoms are adsorbed on the Sc atom. Studied on the molecular orbitals and chemical bonds of the ScH12− cluster, it is found that the ScH12− cluster are mainly the interaction between the 3d atom orbital of Sc and the 1s atom orbital of H. The present study aims at novel nanomaterials for hydrogen storage.

Efficient particulate matter source localization in dynamic indoor environments: An experimental study by a multi-robot system

This study tackles the urgent issue of indoor particulate matter (PM) pollutants, highlighting the critical need for efficient source localization due to its significant health risks. Moving beyond traditional constant-rate gas emission research, we explore real-world PM releases using a multi-robot system, enabling effective PM source localization in dynamically ventilated environments. Initial data collection on airflow and PM concentrations helps optimize our system for mechanical ventilation challenges. Through 90 experiments across six scenarios, we evaluate the improved whale optimization algorithm (IWOA) and improved particle swarm optimization (IPSO) methods, assessing their ability to identify both constant and variable PM sources and their adaptability to different pollutants. The results show a success rate of 73.3% for both methods in locating constant PM sources, with IPSO offering a slight edge in efficiency. Moreover, in tasks involving ethanol vapor, the methods exhibited higher success rates, indicating uniform performance across a variety of pollutants. This consistency, alongside the noted variations in success rates, is primarily attributed to the complexities of PM dispersion. Importantly, our study highlights the methods' adaptability to periodic PM sources with variable release rates, maintaining high success without extra localization steps. This research advances indoor air quality management and emphasizes the importance of flexible, effective pollutant control strategies for public health protection.

Design and multi-criteria optimization and financial assessment of an innovative combined power plant and desalination process

Geothermal power generation programs are crucial due to their long-term sustainability and environmentally friendly characteristics, particularly for potential energy-based applications in the future. Innovative designs can yield improve efficien by mitigating irreversibility, resulting in enhanced power production. Hence, this study investigates an innovative cascade thermal design model that employs geothermal resources to enhance the efficiency and output of low-temperature power generation systems, specifically through the integration of organic flash and organic Rankine cycles. Additionally, this model introduces a novel cogeneration approach by incorporating a desalination unit based on humidification-dehumidification processes, aiming to concurrently produce electricity and fresh water. Employing a combination of thermodynamic analysis and financial assessment, the system's performance was simulated and optimized in MATLAB, using the Multi-Objective Particle Swarm Optimization (MOPSO) method. A sensitivity analysis preceded the optimization, leading to the development of three distinct optimization scenarios focused on balancing power and freshwater production, maximizing exergetic efficiency and net present value, and optimizing fixed capital investment for maximum financial viability. The results highlight the second scenario as particularly effective, achieving a power generation of 254.3 kW, an exergetic efficiency of 44.84 %, and a net present value of $405,099. Conversely, the third scenario offers the best balance between freshwater production capacity (0.504 kg/s), fixed capital investment ($820,822), and a payback period of 7.12 years. This research demonstrates the potential of integrating advanced thermal models with geothermal resources for sustainable and efficient energy and freshwater production, marking a significant step forward in the development of eco-friendly cogeneration systems.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

AbstractTo solve the problems of high sampling requirements and low predictive accuracy resulting from the complexity, uniqueness, and randomness of predicting the risk of the CO2 and N2 injection to enhance coal seam gas drainage (CO2/N2–ECGD) technology. The principal component analysis (PCA) method to reduce the dimensionality of the factor data that contribute to the effect risk of the technology was adopted. And the particle swarm optimization (PSO) method was implemented to search for optimal hyperparameters in support vector machine (SVM) by particle search, as a solution to the traditional SVM hyperparameters optimization problem. A novel risk prediction model using machine learning algorithms for gas injection displacement technology was constructed. The prediction results were tested and compared with those of backpropagation (BP), Random Forest (RF), and Decision Tree (DT) models using data from 29 gas injection displacement field projects in China. The results demonstrated that the SVM model had greater accuracy in prediction than the other three models. Additionally, after PSO optimization and dimensionality reduction, the PCA–PSO–SVM model reached 100% prediction accuracy, while requiring less modeling and operation time. The study provided a reliable and reasonable model for predicting technical effects, along with a theoretical basis for risk management and prevention. First, the technology's influencing indicators were analyzed by examining its mechanisms. Second, we utilized the PCA method to reduce the dimensionality of the factor data that contribute to the risk of the technology's effects. Third, we implemented the PSO method to search for optimal hyperparameters in the SVM through particle search, as a solution to the traditional SVM hyperparameters optimization problem. Finally, the prediction results were tested and compared with those of BP, RF, and DT models using data from 29 gas injection displacement field projects in China. The SVM model was found to have greater accuracy in prediction than the other three models. After PSO optimization and dimensionality reduction, the PCA–PSO–SVM model achieved 100% prediction accuracy while requiring less modeling and operation time. The study presents a valid and reasonable model for predicting technical effects and a theoretical basis for risk management and prevention.

Identifying Pd9OX as the optimum catalyst for the direct synthesis of H2O2 through microkinetic modeling with coverage effects

Identifying efficient active sites for the direct synthesis of hydrogen peroxide over Pd-based catalysts has been a subject of considerable debate. In this study, we employ particle swarm optimization method and density functional theory to explore the H2O2 synthesis mechanism on Pd, PdO, and the partially oxidized surface (Pd9OX). A comprehensive mechanism for Pd9OX is elucidated, and subsequent coverage-dependent kinetic analysis allows for a quantitative assessment of catalytic performance at the interphase. Our findings conclusively establish that the interphase between Pd and PdO represents the optimal active site. Phase diagram analysis further aids in determining stable structures under reaction conditions. At 298.15 K and under oxygen balance, the Pd9O6 surface remains stable throughout the reaction, demonstrating high activity and selectivity. This work underscores the significance of the interphase in comprehending catalytic performance and unveils promising avenues for optimizing catalyst performance by controlling reaction conditions and surface composition.

Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles

From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.

A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm optimization (DWPSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) algorithm is proposed to construct a particle filter to estimate the SOE of a lithium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model, and use the LSSVM model to optimize the SOE estimates obtained by the PF algorithm twice to improve the accuracy of SOE estimation for lithium-ion batteries. The feasibility of the proposed algorithm is verified using two complex operating conditions and at three different temperatures. The validation results show that the maximum error of SOE estimation of the proposed algorithm is 0.0284 for a wide temperature range under Beijing Bus Dynamic Stress Test (BBDST) condition, and 0.0226 for a wide temperature range under Dynamic Stress Test (DST) condition. The proposed algorithm significantly improves the accuracy of SOE estimation and provides a reference for fundamental applications of lithium-ion batteries.

Enhanced harris hawks optimization based load frequency control of multi area microgrid based water treatment plant with consideration of 3DOF-(FO-PIDN)/(TIDN) controller

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply.

Automated diagnosis of acute cerebral ischemic stroke lesions from diffusion-weighted magnetic resonance images in assessment with modified Rankin Scale

Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r = 0.820, p < 0.0001) and perimeter (r = 0.928, p < 0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis.