Binary Particle Swarm Optimization Research Articles

Power Loss Reduction and Reliability Improvement of Radial Distribution Systems Using Optimal Capacitor Placement Technique

Improving reliability and power quality in Radial Distribution Systems (RDS) is of large significance to ensure the provision of electricity within a reliable and acceptable standard to consumers with increasing load requirements. An Optimal Capacitor Placement (OCP) technique is used in the present work to achieve the highest power quality and system reliability in a balanced manner at the same time. The proposed technique has been tested with 69 typical IEEE RDS buses using the Improved Binary Particle Swarm Optimization (IBPSO) algorithm. The proposed algorithm shows a high ability to find the best location and size of injected capacitors inside the RDS to implement a single-objective function for minimization of Active Power Loss (APL). The simulation results obtained from the MATLAB environment show that the OCP technique has a significant potential to enhance RDS reliability, bus voltage, and loss reduction as compared to other previous work.

A Novel Power Quality Monitor Placement Method Using Adaptive QuantumInspired Binary Particle Swarm Optimization

This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function consists of two functions which are based on monitor overlapping index and sag severity index. In this algorithm, the standard quantum-inspired binary PSO is modified by applying the concept of artificial immune system as an adaptive element to make it more flexible towards better quality of solution and computational speed. The proposed algorithm is applied on the IEEE 30-bus transmission system and the IEEE 34-node distribution system and compared to the conventional PSO.

A coding based metasurface absorber with triple circular ring resonator for broadband RCS reduction and high EMI shielding effectiveness

In this paper, a novel coding-based metasurface absorber (CMA) featuring a triple circular ring resonator is introduced for broadband radar cross-section (RCS) reduction and high electromagnetic interference (EMI) shielding effectiveness. The coding element comprises a triple circular ring resonator generating a 1-bit coding scheme (‘0’ and ‘1’ element) with a reflection phase difference of 180° ± 45° across the frequency range of 7.5 GHz–15.5 GHz by employing varying dimensions for the outer circular ring. A most affordable FR-4 substrate with 1.6 mm thickness is used to design the CMA structure. Binary Particle Swarm Optimization (BPSO) algorithm combined with array theory is employed to strategically position unit cells within the subarray. Simulations demonstrate a significant RCS reduction of −10 dBsm across the entire 7.5 GHz–15.5 GHz band. Furthermore, the 2D and 3D scattering patterns observed at 8.26 GHz, 10.56 GHz, and 13.30 GHz serve as additional proof of the remarkable effectiveness of the optimized pattern in reducing bi-static RCS. The optimized CMA structure also exhibits absorption levels exceeding 80 % at different resonance frequency while maintaining high shielding effectiveness. To validate these findings, a prototype of the optimized CMA structure is fabricated, and experimental investigations are conducted. The measured results closely align with the simulated outcomes, with minor discrepancies observed outside the designated frequency band. Based on comprehensive simulation and experimental analyses, the optimized CMA structure emerges as a promising choice for achieving broadband RCS reduction and high EMI shielding effectiveness in practical applications.

Design a Compact and Miniaturized Unit Cell in Substrate Integrated Plasmonic Waveguides

This paper presents a novel procedure to design a compact and miniaturized unit cell in the substrate integrated plasmonic waveguide (SIPW) structures. The slot of the unit cell is automatically obtained in this method without needing any prior knowledge of its shape. The method is based on topology optimization through pixelization of the slot surface in the unit cell and uses a combination of binary particle swarm optimization (BPSO) and commercial electromagnetic (EM) software. With this method, first, the shapes of the two unit cells are engineered to arbitrarily reduce the high cut-off frequency. Then, two low-pass filters consisting of the proposed unit cells are designed and simulated. To further verify the proposed procedure, one of the filters is fabricated, whose measurement results are in good agreement with the simulation results. This filter provides a pass band of 5.6 to 6.3 GHz and has dimensions of 0.82λg×0.22λg. The proposed method has the potential to minimize microwave and terahertz devices.

Reverse design and optimization of digital terahertz bandpass filters

In this paper, an ingenious reverse design method is applied to the design and optimization of terahertz bandpass filters in order to achieve standardized design of high-performance terahertz functional devices. An equivalent model of subwavelength metasurface mapped to digital space is established. Based on ideal objective functions and constraints, intelligent algorithms begin a bold journey to explore the vast potential structure in the solution space. Through iterative refinement, the algorithm reveals optimal structural patterns, unlocking areas of unparalleled performance. The direct binary search (DBS) algorithm and the binary particle swarm optimization (BPSO) algorithm are compared in optimization process. When using the DBS algorithm to optimize the design area, it takes a long time to poll the logic states of all pixel units point by point, and it is easy to get stuck in the local optimal value. However, BPSO algorithm has stronger global search capabilities, faster convergence speed, and higher accuracy. Through a comprehensive comparison of the device performance optimized by the two algorithms, the solution optimized by BPSO algorithm has better out-of-band suppression performance and a narrower full width at half peak, but slightly lower transmittance at the center frequency. The bandpass filter has a center frequency of 0.51 THz, a bandwidth of 41.5 GHz, and an insertion loss of -0.1071 dB. When considering computational efficiency, DBS algorithm lags behind, the simulation time is 11550 s, while BPSO algorithm only needs 9750 s. Compared with the traditional forward design, the reverse design method can achieve the narrower band, lower insertion loss, better out-of-band suppression and polarization stability. The fine structural changes of the optimal results have a significant influence on spectral performance, demonstrating the superiority and uniqueness of reverse design. This technology contributes to the design and optimization of high-performance and novel functional devices.

Modeling and simulation of complex emergency dispatch based on BIPSO

In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data.

Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes. Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases. In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values. The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.

Residential Appliances Scheduling Using Binary Sparrow Search Algorithm For Demand Side Management

The rise in electricity consumption in conventional buildings has resulted in increased electricity rates in numerous countries. Additionally, inefficient energy management leads to higher carbon emissions. In contrast, modern smart buildings integrate advanced energy management systems. This paper presents a multi-objective load-shifting system that aims to minimize electricity costs, load peaks, and waiting time when scheduling daily appliance usage using Binary Sparrow Search Algorithm (BSAA). A comparison of BSSA and Binary Particle Swarm Optimization (BPSO) methodologies shows that SSA is superior in achieving cost reduction (27.6%) and Peak-to-Average-Ratio (PAR) minimization (40.32%) compared to BPSO, which achieved a cost reduction of 13.42% and a PAR minimization of 36.05%. However, the mean waiting times of the two approaches are similar.

Study on binary-amplitude far-field super-resolution achromatic focusing devices

The far-field super-resolution focusing devices possess characteristics such as super-resolution focusing, achromatic, small size and easy machining, which make them highly promising in optical imaging, optical microscopy and lithography. In this work, we propose a binary-amplitude modulation-based method for generating far-field super-resolution achromatic focusing. By using the principles of optical super-oscillation, combined with angular spectral diffraction theory and binary particle swarm optimization (BPSO), we optimize the binary amplitude-type far-field super-resolution focusing devices, which have an identical radius of 100<i>λ</i> but different focal lengths: <i>λ</i><sub>1</sub> = 405 nm, <i>λ</i><sub>2</sub> = 532 nm and <i>λ</i><sub>3</sub> = 632.8 nm, respectively. Additionally, an achromatic metalens is integrated by using Boolean AND operation. To assess the feasibility of our proposed approach, numerical simulations are conducted via COMSOL Multiphysics employing FEM analysis. The simulation results demonstrate that the generated spots are located at 25.105<i>λ</i>, 25.106<i>λ</i>, and 25.105<i>λ</i>, respectively. The corresponding full width at half maximum (FWHM) values are 0.441<i>λ</i><sub>1</sub> (0.179 μm), 0.469<i>λ</i><sub>2</sub> (0.249 μm) and 0.427<i>λ</i><sub>3</sub> (0.270 μm), which are smaller than the Abbe diffraction limit, and the far-field super-resolution achromatic focusing is realized. The sidelobe ratios are at low levels, i.e. 12.5%, 12.6%, and 14.2%. The binary amplitude-type far-field super-resolution achromatic devices have the advantages of easy machining, achromatism and super-resolution, and are suitable for miniaturization and integration of optical systems.

Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks

Satellite edge computing shows promise in delivering seamless, low-latency services in challenging contexts such as deserts, oceans, and during natural disasters. Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node. However, for end-to-end tasks, exemplified by applications like satellite-based remote sensing and forestry monitoring, the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission. This aspect substantially limits the effectiveness of traditional computation offloading methods. In this paper, we formulate a joint computation offloading, routing, and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks. Moreover, to reduce the complexity of this nondeterministic polynomial-time hard (NP-hard) problem, we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem. An algorithm based on binary particle swarm optimization for joint computation offloading, routing, and multiple resource allocation optimization problem is proposed. Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.

Evaluating the Effectiveness of the Binary PSO Method in Feature Selection to Improve the Detection of Android Botnets

Evaluating the Effectiveness of the Binary PSO Method in Feature Selection to Improve the Detection of Android Botnets

Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization

Various cultivars of date fruits distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally lack variability in date fruits, since various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit according to their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest-Neighbor (KNN) classifier. Considering the parameters applied to develop the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN.

An improved binary particle swarm optimization algorithm for clinical cancer biomarker identification in microarray data

Background and ObjectiveThe limited number of samples and high-dimensional features in microarray data make selecting a small number of features for disease diagnosis a challenging problem. Traditional feature selection methods based on evolutionary algorithms are difficult to search for the optimal set of features in a limited time when dealing with the high-dimensional feature selection problem. New solutions are proposed to solve the above problems. MethodsIn this paper, we propose a hybrid feature selection method (C-IFBPFE) for biomarker identification in microarray data, which combines clustering and improved binary particle swarm optimization while incorporating an embedded feature elimination strategy. Firstly, an adaptive redundant feature judgment method based on correlation clustering is proposed for feature screening to reduce the search space in the subsequent stage. Secondly, we propose an improved flipping probability-based binary particle swarm optimization (IFBPSO), better applicable to the binary particle swarm optimization problem. Finally, we also design a new feature elimination (FE) strategy embedded in the binary particle swarm optimization algorithm. This strategy gradually removes poorer features during iterations to reduce the number of features and improve accuracy. ResultsWe compared C-IFBPFE with other published hybrid feature selection methods on eight public datasets and analyzed the impact of each improvement. The proposed method outperforms other current state-of-the-art feature selection methods in terms of accuracy, number of features, sensitivity, and specificity. The ablation study of this method validates the efficacy of each component, especially the proposed feature elimination strategy significantly improves the performance of the algorithm. ConclusionsThe hybrid feature selection method proposed in this paper helps address the issue of high-dimensional microarray data with few samples. It can select a small subset of features and achieve high classification accuracy on microarray datasets. Additionally, independent validation of the selected features shows that those chosen by C-IFBPFE have strong correlations with disease phenotypes and can identify important biomarkers from data related to biomedical problems.

A novel scheduling approach of stochastic cogeneration model in power system environment using improved civilized swarm search algorithm to reduce cost and carbon emission

This study explores a cogeneration system's optimal commitment and generation scheduling while considering power and heat demand uncertainty. The deterministic model corresponding to the Cogeneration Based Unit Commitment Problem (CBUCP) cannot express the uncertainties related to input data and energy demand. In the present work, the stochastic Multi-Objective Cogeneration Based Unit Commitment Problem (MO-CBUCP) aims to obtain the optimal generation schedule at minimum operational cost and emission, considering uncertainties in the heat and power demand. To deal with continuous and binary variables of MO-CBUCP and obtain an optimal solution, an effective combination of nature-inspired techniques such as Civilized Swarm Search Algorithm (CSSA) and Binary Particle Swarm Optimization (BPSO) is employed for the cogeneration-based test system. To improve the exploration ability of CSSA, the predator and seasonal effect has been incorporated, as the movement of particles is affected by weather changes. Improved CSSA effectively balances the exploration and exploitation during the optimization. The results of the proposed technique are compared with other optimization techniques to show efficacy. The comparative analysis shows that the cogeneration plant significantly affects operating cost and emission for the deterministic and stochastic models.

Optimized design of nano-photonic devices for temperature self-regulating on vanadium dioxide thin films

Phase change materials can enable temperature self-regulation due to their drastic changes in optical properties accompanying the phase transition. Significant reduction of the optical absorption after the transition is the key ingredient for an enhanced regulating performance. However, the absorptivity of unpatterned vanadium dioxide (VO2) thin films can hardly be reduced after phase transition at visual-to-infrared band. In this work, we combine the direct binary search (DBS) and particle swarm optimization (PSO) algorithms for an optimized design of temperature self-regulating nano-photonic devices on VO2 thin films. For a given incident wavelength, a pixelated structure is firstly inverse-designed by the DBS algorithm which maximizes the absorption contrast before and after the transition. To overcome fabrication challenges as pixel size is at deep sub-wavelength scale, the pixelated structure can then be replaced by geometric shapes which are more tractable in manufacturing processes. The geometrical parameters are optimized by the PSO algorithm where our optimized device brings the absorptivity down to 33% after the transition. These results provide an effective way for the inverse design of optimized nano-photonic structures based on phase change materials.

Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling

In this paper, the problem of scheduling smart homes (SHs) residential loads is considered aiming to minimize electricity bills and enhance the user comfort. The problem is addressed as a multi-objective constraint mixed-integer optimization problem (CP-MIP) to model the constrained load operation. As the CP-MIP optimization problem is non-convex, a novel hybrid search technique, that combines the Relaxation and Rounding (RnR) approach and metaheuristic algorithms to enhance the accuracy and relevance of decision variables, is proposed. This search technique is implemented through two stages: the relaxation stage in which a metaheuristic technique is applied to get the optimal rational solution of the problem. Whereas, the second stage is the rounding process which is applied via stochastic rounding approach to provide a good-enough feasible solution. The scheduling process has been done under time-of-use (ToU) dynamic electricity pricing scheme and two powering modes (i.e., powering from the main grid only or powering from a grid-tied photovoltaic (PV) residential power system), in addition, four metaheuristics [i.e., Binary Particle Swarm Optimization (BPSO), Self-Organizing Hierarchical PSO (SOH-PSO), JAYA algorithm, and Comprehensive Learning JAYA algorithm (CL-JAYA)] have been utilized. The results reported in this study verify the effectiveness of the proposed technique. In the 1st powering mode, the electricity bill reduction reaches 19.4% and 20.0% when applying the modified metaheuristics, i.e. SOH-PSO and CL-JAYA, respectively, while reaches 56.1%, and 54.7% respectively in the 2nd powering scenario. In addition, CL-JAYA superiority is also observed with regard to the user comfort.

Sub-diffraction metalens for generating longitudinal bifoci and optical needles

Lenses are a fundamental component of optical systems. Bifoci and optical needle devices have excellent application potential in many optical systems. Conventional lenses are limited by their diffraction limits, and the spot size has a considerable influence on the resolution of optical and microscopic images. Optical super-oscillation is a novel technique, to the best of our knowledge, for far-field sub-diffraction focusing. In this study, we proposed a binary-amplitude super-oscillatory lens (SOL) approach for generating bifoci and optical needles (ON), and it was based on the angular spectrum method (ASM) and a binary-particle-swarm optimization (BPSO) algorithm. We reported a class of binary-amplitude-based Bifoci- and ON-SOLs that generated sub-diffraction bifoci and optical needles. Sub-diffraction bifoci with a transverse range of 0.401λ−0.522λ were recorded for a work wavelength of λ=632.8nm. The generated optical needle had a sub-diffraction length of 4.122λ, and the super-oscillation region was 2.083λ long. This provides potential applications for further super-resolution imaging, optical communication, and precision manufacturing.

Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization

Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization

Parameter Optimization of Energy Storage Virtual Synchronous Machine Based on Particle Swarm Optimization Algorithm

Energy transformation is a severe challenge and major demand faced by China’s sustainable development, and new energy development has become a key driving force for energy transformation. The issue of system stability is brought to light by the steadily rising share of renewable energy sources like wind and solar, which in turn places greater demands on the inertia support capacity of power plants. In order to replicate the dynamic equations of a conventional synchronous machine and add inertia and damping to the system, a new technology known as virtual synchronous machine (VSM) control has arisen. Therefore, improving the system’s absorption capacity of new energy generation and ensuring its safe and stable operation are both greatly aided by the study of energy storage VSM control technology’s grid stability and parameter design methods. Although VSM control adds inertia to a system, which is good for stability, it also complicates the system’s ability to withstand tiny disturbances. This paper investigates the small disturbance stability of VSM-controlled power generating and storage systems. The stability analysis method of storage VSM when subjected to random power sources is investigated further, and a method for optimising the system’s parameters is proposed. VSM with optimised parameters can provide sufficient inertia and damping under complex operating conditions, as shown by simulation results in real-world systems. Finally, the proposed method’s efficacy was assessed with the help of an enhanced binary particle swarm optimisation (EBPSO) algorithm, and the outcomes confirmed its superiority.