Particle Swarm Optimization Method Research Articles

Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study

Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

Improved intelligent model predictive controller for the nuclear power reactor system

Abstract Over the last two decades, extensive research has been conducted to enhance the control performance of reactor power. Various methodologies have been suggested and implemented to achieve optimal power control in nuclear reactors. However, due to the diverse characteristics and inherent uncertainties in the models, devising optimal controllers for nuclear systems remains a complex task. To address this, numerous approaches have been adopted to ensure controllability and resilience, aiming for an optimal nuclear power reactor controller. The Model Predictive Control (MPC) algorithm has garnered significant attention as a viable approach to boost operational efficiency and overall system utility. In this research, Particle Swarm Optimization (PSO) method and MPC controller are combined together to form a novel algorithm termed PSO-MPC, aiming to amplify the system’s performance and overcomes the local minima problem that basically happens when using MPC controller. First, MPC controller is applied to the nuclear reactor model, then the suggested technique PSO-MPC also is applied to the system and a comparison of the outcomes using both techniques is done. The results demonstrate enhanced system response using the innovative technique.

Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.

Global Optimization of Molybdenum Subnanoclusters on Graphene: A Consistent Approach toward Catalytic Applications.

The development of novel subnanometer clusters (SNCs) catalysts with superior catalytic performance depends on the precise control of clusters' atomistic sizes, shapes, and accurate deposition onto surfaces. The intrinsic complexity of the adsorption process complicates the ability to achieve an atomistic understanding of the most relevant structure-reactivity relationships hampering the rational design of novel catalytic materials. In most cases, existing computational approaches rely on just a few structures to draw conclusions on clusters' reactivity thereby neglecting the complexity of the existing energy landscapes thus leading to insufficient sampling and, most likely, unreliable predictions. Moreover, modeling of the actual experimental procedure that is responsible for the deposition of SNCs on surfaces is often not done even though in some cases this procedure may enhance the significance of certain (e.g., metastable) adsorption geometries. This study proposes a novel systematic approach that utilizes global search techniques, specifically, the particle swarm optimization (PSO) method, in conjunction with ab initio calculations, to simulate all stages in the beam experiments, from predicting the most relevant SNCs structures in the beam and on a surface, to their reactivity. To illustrate the main steps of our approach, we consider the deposition of Molybdenum SNC of 6 Mo atoms on a free-standing graphene surface, as well as their catalytic properties with respect to the CO molecule dissociation reaction. Even though our calculations are not exhaustive and serve only to produce an illustration of the method, they are still able to provide insight into the complicated energy landscape of Mo SNCs on graphene demonstrating the catalytic activity of Mo SNCs and the importance of performing statistical sampling of available configurations. This study establishes a reliable procedure for performing theoretical rational design predictions.

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Abstract The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

Active micro-vibration isolation system for adaptive vibration suppression tests using piezoelectric stack actuator

An active micro-vibration isolation system for adaptive vibration suppression using piezoelectric stack actuators is presented, along with the discrete time modelling method, real-time adaptive controller design, and hardware implementation. The system comprises piezoelectric stack actuators, capacitive displacement sensors, power amplifiers, signal conditioners, and a real-time computer. A discrete asymmetric Bouc-Wen model is developed to characterize the nonlinear behavior of the piezoelectric stack actuators. System identification is conducted using an improved particle swarm optimization method, specifically the Hybrid PSO-Jaya algorithm, which sequentially integrates the PSO algorithm with the Jaya algorithm. Additionally, an improved adaptive filtering algorithm employing affine projection, referred to as the FXAPA algorithm, is introduced to facilitate real-time control. The efficacy of the proposed active micro-vibration isolation technology is validated through micro-vibration suppression tests.

AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

High performance deep violet InGaN TQW laser diode through multi-layer thickness optimization using particle swarm optimization method

The paper details the optimization of efficiency parameters for a InGaN TQW laser diode using the particle swarm optimization algorithm through SILVACO software. By adjusting layer thickness based on output power, slope efficiency, and threshold current, significant enhancements were achieved. Three optimized laser diodes (LD1, LD2, LD3) were compared with a primary LD, revealing notable improvements in output power and overall performance. Analysis of carrier recombination rates in the active region highlighted increased stimulated recombination and decreased non-radiative recombination, particularly in the n-side well. The study also showed a more uniform radiative recombination in the quantum wells of the optimized LDs, especially in LD3. Investigation of electron and hole concentrations, current densities, and energy levels demonstrated higher electron and hole current density in the optimized LDs, with LD3 exhibiting substantial improvements over the primary LD. Notably, the optimized LDs displayed reduced threshold current and improved slope efficiency compared to the primary LD. The study identified optimal layer thicknesses based on various cost functions, resulting in significant enhancements in output power (0.561 W), slope efficiency (2.515 W/A), and reduced threshold current (less than 0.029 A), indicative of enhanced TQW laser diode performance. Overall, the research showcases the effectiveness of the particle swarm optimization approach in optimizing GaN-based TQW laser diodes, leading to improved efficiency characteristics and demonstrating the potential for enhanced performance in practical applications.

Heat energy utilization of a double-flash geothermal source efficiently for heating/electricity supply through particle swarm optimization method

The principal aim of this article is to optimize the thermal and electrical efficiency of a geothermal combined heat and power system through metaheuristic particle swarm optimization (PSO) method. The objective of this research is to conduct a thorough analysis of the incorporation of metaheuristic PSO technique, with a specific emphasis on the potential advantages and obstacles associated with the utilization of metaheuristic approaches in improving the effectiveness of geothermal energy systems. The utilization of a double-flash geothermal system in conjunction with a transcritical carbon dioxide Rankine cycle is utilized for the co-generation of electricity and thermal energy. The research utilized a PSO method to enhance power generation, heating capacity, and overall system efficiency. The PSO algorithm was employed to determine the optimum operational parameters for a pressure level of 820 kPa and a pressure ratio of 1.59, leading to the maximization of power output to 2591.4 kW The PSO algorithm effectively identified the optimal operational parameters as a pressure of 820 kPa and a pressure ratio of 1.59, resulting in the achievement of a peak power output of 2591.4 kW. The methodology has determined that a pressure of 916.4 kPa and a pressure ratio of 1.5 represent the optimal parameters for achieving a maximum heating capacity of 12329.1 kW.

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

The Propagation of Congestion on Transportation Networks Analyzed by the Percolation Process

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO2 emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.

An adaptive snow ablation-inspired particle swarm optimization with its application in geometric optimization

In response to the shortcomings of particle swarm optimization (PSO), such as low execution efficiency and difficulty in overcoming local optima, this paper proposes a multi-strategy PSO method incorporating snow ablation operation (SAO), known as SAO-MPSO. Firstly, Cubic initialization is performed on particles to obtain a good initial environment. Subsequently, SAO and PSO are combined in parallel, and a balanced search mechanism led by multiple sub-populations is devised, significantly improving the search efficiency of overall population. Finally, the degree day method of SAO is introduced, and particles are endowed with memory of environmental changes to prevent premature convergence of PSO, while balancing the exploration and exploitation (ENE) capabilities in later phases. All adaptive parameters are used throughout this method in place of fixed parameters to improve the robustness and adaptability. For a comprehensive analysis of SAO-MPSO, its good ENE ability is verified on CEC 2020 and CEC 2022 and this method is compared with existing improved PSO versions on both test sets. The results show that SAO-MPSO has certain advantages in the comparison of similar improved algorithms. In order to further validate the strength of SAO-MPSO in dealing with nonlinear optimization problems (OPs) with strong constraints, firstly, based on the ball Wang-Ball (BWB) curve, a combined BWB (CBWB) curve is constructed, and a construction method for CBWB curves that satisfy G1 and G2 continuity is derived. Then, with the energy minimization and scale parameters of the CBWB curve as the optimization objective and variables respectively, a shape optimization model that satisfies G2 continuity is established. Finally, three numerical optimization examples based on this model are solved using SAO-MPSO and compared with 10 other methods. The results show that the energy obtained by SAO-MPSO is the smallest, which verifies the effectiveness of this method applied to shape OPs of CBWB curve.

A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga2O3 Systems.

The atomic configurations and concentrations of intrinsic defects profoundly influence the electrical and optical properties of the semiconductor materials. This influence is particularly significant in the case of β-Ga2O3, which is a highly promising ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. We design an effective algorithm for the explicit identification of various intrinsic point defects in the β-Ga2O3 lattice, which constitutes the integration of the particle swarm optimization (PSO) and K-means clustering (K-MC) methods. Our algorithm attains the recognition accuracy exceeding 95%. Finally, the algorithm is applied to dynamic simulations, where the feasibility of dynamic real-time detection is explored.

Multi‐objective optimization of inductive power transfer system with reconfigurable topology for misalignment tolerance

AbstractAt present, most of the analyses or studies about inductive power transfer (IPT) with constant current (CC) output and constant voltage (CV) output are carried out without considering misalignment conditions or different gaps. An IPT system satisfying battery charging demand and anti‐misalignment requirements simultaneously is infrequent. This paper proposes a multi‐objective particle swarm optimization method of IPT reconfigurable topology to realize CC and CV modes at varying resistance conditions and wide coupling ranges. The output characteristics of an inductor–capacitor–capacitor (LCC)–LCC compensation circuit have been explored, and it is found that the secondary‐side compensated capacitors have a greater impact on the output power, which can be used to improve power regulation ability accompanied by coupling varying. Eight optimization compensated parameters of the reconfigurable topology are obtained from the Pareto front to achieve the required CC and CV charging outputs. By switching the compensated capacitors, the selected parameters can make the current and voltage fluctuation less than 9.3% and 7.9%, respectively, during the coupling charging range from 0.3 to 0.22. Moreover, primary zero voltage switching operation is achieved to enable high efficiency. The simulation and the experimental verification are carried out to verify the validity of the proposed method.

Empirical interatomic potential development and classical molecular dynamics simulation of monolayer group-III monochalcogenides: insights into thermal transport properties

Abstract This research addresses the lack of comprehensive studies utilizing classical molecular dynamics simulations for monolayer group-III monochalcogenide materials. These materials, including GaS, GaSe, and InSe, have shown promise for diverse applications but lack well-defined empirical interatomic potentials in the literature. This study is concentrated on the development of empirical interatomic potential parameters for these materials using the particle swarm optimization method, filling a gap in the literature regarding classical molecular dynamics simulations. The parameters are optimized based on fundamental physical characteristics such as the lattice constants, bond lengths, phonon dispersions, and the equation of state, obtained from first-principles calculations. The developed potential parameters are then employed to predict lattice thermal conductivity through non-equilibrium classical molecular dynamics simulations, providing insights into the thermal transport properties of these materials.

The Application of an Improved LESS Dung Beetle Optimization in the Intelligent Topological Reconfiguration of ShipPower Systems

To address the shortcomings of the Dung Beetle Optimization (DBO) algorithm in ship power-system fault reconfiguration, such as low population diversity and an imbalance between global exploration and local exploitation, the authors of this paper propose an improved Dung Beetle Optimization (LESSDBO) algorithm. The improvements include optimizing the initial population using Latin hypercube sampling and an elite population strategy, optimizing parameters with an improved sigmoid activation function, introducing the sine–cosine algorithm (SCA) for position update optimization, and performing multi-population mutation operations based on individual quality. The LESSDBO algorithm was applied to simulate the fault reconfiguration of a ship power system, and it was compared with the traditional DBO, Genetic Algorithm (GA), and Modified Particle Swarm Optimization (MSCPSO) methods. The simulation results showed that LESSDBO outperformed the other algorithms in terms of convergence accuracy, convergence speed, and global search capability. Specifically, in the reconfiguration under Fault 1, LESSDBO achieved optimal convergence in seven iterations, reducing convergence iterations by more than 30% compared with the other algorithms. In the reconfiguration under Fault 2, LESSDBO achieved optimal convergence in eight iterations, reducing convergence iterations by more than 23% compared with the other algorithms. Additionally, in the reconfiguration under Fault Condition 1, LESSDBO achieved a minimum of four switch actions, which is 33% fewer than the other algorithms, on average. In the reconfiguration under Fault Condition 2, LESSDBO achieved a minimum of eight switch actions, which is a 5.9% reduction compared with the other algorithms. Furthermore, LESSDBO obtained the optimal reconfiguration solution in all 50 trials for both Faults 1 and 2, demonstrating a 100% optimal convergence probability and significantly enhancing the reliability and stability of the algorithm. The proposed method effectively overcomes the limitations of the traditional DBO in fault reconfiguration, providing an efficient and stable solution for the intelligent topology reconfiguration of ship power systems.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.