Binary Particle Swarm Optimization Research Articles

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

A novel importance-guided particle swarm optimization based on MLP for solving large-scale feature selection problems

Feature selection is a crucial data preprocessing technique that effectively reduces the dataset size and enhances the performance of machine learning models. Evolutionary computation (EC) based feature selection has become one of the most important parts of feature selection methods. However, the performance of existing EC methods significantly decrease when dealing with datasets with thousands of dimensions. To address this issue, this paper proposes a novel method called importance-guided particle swarm optimization based on MLP (IGPSO) for feature selection. IGPSO utilizes a two stage trained neural network to learn a feature importance vector, which is then used as a guiding factor for population initialization and evolution. In the two stage of learning, the positive samples are used to learn the importance of useful features while the negative samples are used to identify the invalid features. Then the importance vector is generated combining the two category information. Finally, it is used to replace the acceleration factors and inertia weight in original binary PSO, which makes the individual acceleration factor and social acceleration factor are positively correlated with the importance values, while the inertia weight is negatively correlated with the importance value. Further more, IGPSO uses the flip probability to update the individuals. Experimental results on 24 datasets demonstrate that compared to other state-of-the-art algorithms, IGPSO can significantly reduce the number of features while maintaining satisfactory classification accuracy, thus achieving high-quality feature selection effects. In particular, compared with other state-of-the-art algorithms, there is an average reduction of 0.1 in the fitness value and an average increase of 6.7% in classification accuracy on large-scale datasets.

Fitness and historical success information-assisted binary particle swarm optimization for feature selection

Feature selection is a critical preprocessing step in machine learning aimed at identifying the most relevant features or variables from a dataset. Although conventional particle swarm optimization (PSO) has shown efficiency for feature selection tasks, developing an effective PSO algorithm for this task is still challenging. This study proposes a fitness and historical success information-assisted binary particle swarm optimization, denoted by FPSO. The FPSO is developed by integrating different search strategies, including a weighted center-based approach, historical information-based acceleration coefficients, and selection operation. These strategies are embedded into the FPSO to enhance the levels of exploration and exploitation based on the fitness value of particles and their historical search status. In the FPSO, the transfer function is also added to transform the continuous search space into binary search space. Experimental validation and comparison with seven other metaheuristic algorithms on 24 datasets verify the effectiveness of the FPSO in eliminating irrelevant and redundant features.

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Designing broadband cross-polarization conversion metasurfaces using binary particle swarm optimization algorithm

Owing to their disadvantages of broadband discontinuity, infinite value, or incapability of distinguishing cross-polarization conversions from co-polarization conversions, the traditional linear polarization discrimination methods relating to the phase difference Δφ, the ellipticity angle β, or the axial ratio are inapplicable to the software-based optimal design of broadband cross-polarization conversion metasurfaces (CPMs) consisting of subblocks. Therefore, we introduce the pseudo ellipticity angle β' to characterize the cross-polarization conversion. And based upon the coding metasurfaces and β', this work first employs the Python-programmed binary particle swarm optimization algorithm to accomplish the optimal design of reflective broadband CPMs. Then, the obtained CPM is further simplified by reprocessing the relatively isolated subblocks, which results in the final CPM. The simulated results show that the final CPM realizes the broadband cross-polarization conversion with β' ≥ 0.4π and a relative bandwidth of 36.1 % from 11.66 GHz to 16.79 GHz. Moreover, the measured results are in excellent agreements with the simulated ones, which validates the proposed design method. It advances the broadband CPMs design from manual to automated method.

BINARY FRUIT FLY OPTIMIZATION ALGORITHM FOR COUNTING THE NUMBER OF SPANNING TREES IN NETWORKS

The NP-hard problem of determining the number of spanning trees of graphs is examined in this paper. A spanning tree of a graph is a tree such that (i) it is a subgraph of (i.e., that includes only edges from ), and (ii) it includes every vertex of . The most classical interest concerning a spanning tree is the number of spanning trees or the complexity of the graph denoted by We propose the first attempt to use a binary version of the fruit fly optimization algorithm (BFFOA) to compute the minimal spanning tree of a graph in a heuristic manner. The fruit fly of BFFOA is binary encoded and used to represent which one of the vertices of the graph belongs to the spanning tree of . The feasibility is enforced by repairing the fruit fly such that an extra vertex created from vertices of is added to , and this repairing process is repeated until becomes the spanning tree of . Theoretically computed graph results are used to compare the proposed BFFOA against competing techniques. The proposed BFFOA performs better than the binary Grey Wolf Optimizer (BGWO), the binary Particle Swarm Optimizer (BPSO), the binary Whale Optimizer (BWO), and the binary Multi-verse Optimization (BMVO) algorithms, according to analysis and computational results.

Orthopedic disease classification based on breadth-first search algorithm

Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

A two-phase adaptive large neighborhood search algorithm for the electric location routing problem with range anxiety

In response to environmental pollution and global warming, electric vehicles (EVs) have been widely applied in supply chain, and lots of researches have been focused on the electric location routing problem (ELRP) to optimize the location of battery-swapping stations (BSSs). However, few studies have simultaneously considered the EV’s collaborative behavior and range anxiety in ELRP. Therefore, this study focuses on formulating an ELRP with battery-swapping stations considering both collaborative behavior and range anxiety. The model takes into account the collaboration between the supply and demand sides to share supply chain resources effectively. The detour probability function is employed to handle the uncertainty caused by drivers’ range anxiety during the trip, which can be evaluated through a proposed range anxiety function. In addition, a new two-dimensional matrix-based solution representation is proposed to explore ELRP optimal solutions intuitively and efficiently. To solve the proposed model effectively, a two-phase adaptive large neighborhood search (TALNS) algorithm that integrates the adaptive large neighborhood search (ALNS) algorithm and the extended binary particle swarm optimization (EBPSO) algorithm is proposed. In the EBPSO algorithm, a new position update mechanism and local search strategy are used to strengthen the local search ability. In the ALNS algorithm, multiple destroy and repair operators are developed for the proposed model. Further, to validate the effectiveness and performance of the proposed algorithm, comparison experiments with the Gurobi optimizer and other baseline heuristic algorithms are conducted.

Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization-Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm.

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.

Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram.

Deep contextual reinforcement learning algorithm for scalable power scheduling

This article introduces a deep contextual reinforcement learning (DCRL) optimization algorithm to tackle the NP-hard power scheduling problem. The algorithm is based on a multi-agent simulation environment that decomposes the power scheduling problem into sequential Markov decision processes (MDPs). The simulation environment inherently corrects incorrect decisions and adjusts supply capacities so that the MDPs adhere to the optimization constraints. A deep reinforcement learning (DRL) model is trained on these MDPs to provide optimal solutions. Demonstrating its applicability and effectiveness, the proposed method is evaluated on various test systems with different unit numbers, constraints, and production cost functions. The experimental results show that the proposed method has better performance relative to alternative methods, such as binary alternative moth-flame optimization (BAMFO), binary particle swarm optimization (BPSO), teaching learning-based optimization (TLBO), new binary particle swarm optimization (NBPSO), binary learning particle swarm optimization (BLPSO), quasi-oppositional teaching learning-based algorithm (QOTLBO), binary-real-coded genetic algorithm (BRCGA), hybrid genetic-imperialist competitive algorithm (HGICA), three-stage priority list (TSPL), and hybridized evolutionary algorithm and sequential quadratic programming (HEPSQP). The novelties of the proposed method lie in its adaptability to longer planning horizons and linear dimensionality complexity, rendering it suitable for large-scale power systems.

Binary Particle Swarm Optimization for Fair User Association in Network Slicing-Enabled Heterogeneous O-RANs

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks.

Combinatorial-restless-bandit-based transmitter–receiver online selection of distributed MIMO radar with non-stationary channels

In a distributed multiple-input multiple-output (MIMO) radar for tracking moving targets, optimizing sensible selections of the transmitter–receiver pairs is crucial for maximizing the sum of signal-to-interference-plus-noise ratios (SINRs), as it directly affects the tracking accuracy. In solving the trade-off between exploitation and exploration in non-stationary channels, the optimization problem is modeled by a restless multi-armed bandits model. This paper regards the estimated SINR mean reward as the state of an arm (transceiver channel). The SINR reward of each arm is estimated based on whether it is probed. A closed loop is established between SINR rewards and the dynamic states of targets, which are estimated via the interacting multiple model-unscented Kalman filter. The combinatorial optimized selection of transmitter–receiver pairs at each time is accomplished by using the binary particle swarm optimization with the SINR index fitness function, where the index represents the upper bound on the confidence of the SINR reward. Above all, a multi-group combinatorial-restless-bandit closed-loop (MG-CRB-CL) algorithm is proposed. Simulation results for different scenarios are provided to verify the effectiveness and superior performance of MG-CRB-CL.

Disaster tweet classification using enhanced salp swarm algorithm

Twitter and Facebook are widely recognized as crucial tools for situational information during disasters. Given that the classification of disaster related tweets is computationally challenging due to the high dimension of textual data caused by the redundant and irrelevant features. Hence for optimal feature selection (FS) and classification of disaster tweets, this work utilizes binary salp swarm algorithm (BSSA) and proposed two enhancements over it (PBcSSA). The commensalism phase from symbiotic organisms search (SOS) is integrated with BSSA to enhance its feature space searchability and then its parallel implementation is done using Apache Spark framework to reduce the execution time. The experiments were performed in a cross-disaster setting on nine groups of datasets including biological, earthquake, flood, hurricane, industrial, societal, transportation, wildfire, and environmental. The proposed PBcSSA combined with the Naive Bayes (NB) classifier in wrapper mode and its performance is compared with standard BSSA, binary sine cosine algorithm (BSCA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary whale optimization algorithm (BWOA). The experimental results reveal that the proposed PBcSSA outperforms other algorithms in disaster tweet classification and achieved highest average F1-score with lowest feature set in a reduced execution time.

A hybrid Feature Selection Method Based on Binary PSO Algorithm for Microarray Data Classification

Microarray technology produces data with a large number of dimensions. The abundance of dimensions in the data makes it challenging for machine learning algorithms to extract meaningful information from it. To overcome this limitation, feature selection (FS) techniques can be applied to reduce the dimensionality of the data. FS is a crucial preprocessing step that allows for the handling of high-dimensional data and facilitates more effective and efficient processing. The primary objective of this paper is to develop an efficient FS method that can effectively reduce the dimensionality of microarray data. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized in the first filtering stage to extract informative features. The best feature subset is then determined by using a Particle Swarm Optimization (PSO) algorithm as a wrapper feature selector to identify ideal features. The proposed approach is evaluated using microarray datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared with other algorithms. The experimental findings indicate that the proposed approach outperforms the benchmark methods in terms of classification accuracy.

A binary particle swarm optimization-based pruning approach for environmentally sustainable and robust CNNs

Deep Convolutional Neural Networks (CNNs), continue to demonstrate remarkable performance across various tasks. However, their computational demands and energy consumption present significant drawbacks, restricting their practical deployment and contributing to a substantial carbon footprint. This paper addresses this challenge by proposing a novel method named Binary Particle Swarm Optimization Layer Pruner (BPSO-LPruner), aimed at achieving substantial computational reduction and mitigating environmental impact during CNN inference. BPSO-LPruner utilizes a constrained Binary Particle Swarm Optimization for CNN layer pruning, integrating a masked-bit strategy and a new population initialization strategy to enhance search performance. We illustrate the effectiveness of our method in reducing model computational costs and carbon footprint emissions while improving performance across multiple models (VGG16, VGG19, DenseNet-40, ResNet18, ResNet20, ResNet34, ResNet44, ResNet56, ResNet110, ResNet50, and MobileNetv2) and diverse datasets (CIFAR-10, CIFAR-100, Tiny-ImageNet, COVID-19 X-ray dataset). Promising results underscore the performance of the proposed method. Additionally, we demonstrate that layer pruning yields benefits beyond enhanced computational performance. Our experimentation reveals that BPSO-LPruner enhances the model’s reliability and robustness by effectively addressing variations in input data, inherent ambiguity in model parameters, and adversarial images.

Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach

Due to rapid urban development, many indoor and semi-indoor structures, such as underground malls, metro stations, and bus-stops, have emerged. Traditional GNSS outdoor navigation fails in these locations. To meet urban citizens’ needs, we require sustainable ubiquitous localization solutions, leveraging existing WiFi infrastructure. In such applications, processing data at the edge within constrained environments is imperative. Many prior studies neglect the practical challenge of implementing machine learning-based edge device localization, as they assume reliance on cloud servers. Selecting well-distributed data instances that preserve location class boundaries is essential for effective training in such conditions. In this work, we propose an instance selection approach that is modeled by applying a modified Binary Particle Swarm Optimization (BPSO) technique. The k-differentiating neighbor-based measure is incorporated to determine instance hardness while exploring the solution space through BPSO. The work is implemented on three publicly available benchmark datasets that are collected from two university campuses and one shopping mall. The proposed work is found to have achieved an instance reduction of 35% with only 1%–2% decrease in the accuracy measurement and an appreciable error deviation metric of 2.78 m.

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

ABSTRACT Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.

Active distribution network dynamic partitioning method based on the Voltage/Var sensitivity using branch cutting and binary particle swarm optimisation

AbstractTo optimally harness the adjustable capabilities of reactive power sources for voltage control, a dynamic partitioning method that uses reactive power flow tracking for branch cutting through Binary Particle Swarm Optimisation (BPSO) is proposed for Active Distribution Networks (ADNs). Initially, the limitations of existing Voltage/Var Sensitivity (VVS) calculation methods are analysed, leading to the proposition of a novel VVS calculation method capable of capturing variations in source‐load timing characteristics. Subsequently, the fuzzification of the VVS matrix between nodes is used to derive the membership degree matrix. Next, based on the membership relationship between reactive power source nodes, these nodes are pre‐partitioned, and the number of leading nodes and zones alongside are preliminarily determined. Then, the range of the branch to be cut is established, guided by the reactive power flow direction of the branch. Employing the zonal comprehensive coupling degree as the objective function of the BPSO facilitates the identification of optimal branch cutting points, thereby determining the partitioning outcome. Finally, a reactive power reserve check is executed to rectify any non‐compliant zones. In this study, numerical simulations are conducted using the enhanced IEEE 33‐node power system to demonstrate the efficacy of the proposed method.