Binary Particle Swarm Optimization Method Research Articles

A robust state estimation by optimal placement of measurement units considering loads/renewable generations and measurements uncertainty

AbstractThe supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

Dispersion curve engineering for automated topology design of a unit cell in spoof surface plasmon polaritons

In this paper, an automatic design method is proposed for unit cell in spoof surface plasmon polaritons (SSPP) with an almost arbitrary dispersion curve. In this method, the pixel configuration is considered for the unit cell and, by using the binary particle swarm optimization method, the proper topology of the unit cell is explored so as to reach the target dispersion curve. Unlike the traditional method of controlling the dispersion curve, which is performed based on changing the geometric parameters of the predetermined unit cell, in this method, there is no need to know the shape of the unit cell, and the dispersion curve of the modes of SSPP unit cell can be controlled independently with more freedom. Two unit cell samples are designed in order to show the efficiency of the procedure. In the first sample, the dispersion curve is designed to have the lowest asymptotic frequency; in the second sample, the dispersion curve of the second mode is controlled independently from the first mode and is changed arbitrarily. SSPP transmission lines which are related to the unit cells of the two samples are designed, and it is demonstrated that measurement and simulation results are greatly in line with each other.

Binary Comprehensive Learning Particle Swarm Optimization Approach for Optimal Design of Nonlinear Steel Structures with Standard Sizes

This paper proposes the binary comprehensive learning particle swarm optimization (BCLPSO) method to determine the optimal design for nonlinear steel structures, adopting standard member sizes. The design complies with the AISC-LRFD standard specifications. Moreover, the sizes and layouts of cross-brace members, appended to the steel frames, are simultaneously optimized. Processing this design is as challenging as directly solving the nonlinear integer programming problem, where any solution approaches are often trapped into local optimal pitfalls or even do not converge within finite times. Herein, the BCLPSO method incorporates not only a comprehensive learning technique but also adopts a decoding process for discrete binary variables. The former ascertains the cross-positions among the sets of best swarm particles at each dimensional space. The latter converts design variables into binary bit-strings. This practice ensures that local optimal searches and premature termination during optimization can be overcome. The influence of an inertial weight parameter on the BCLPSO approach is investigated, where the value of 0.98 is recommended. The accuracy and robustness of the proposed method are illustrated through several benchmarks and practical structural designs. These indicate that the lowest minimum total design weight (some 3% reduction as compared to the benchmark) can be achieved of about 40% lower than the total number of analyses involved.

A Hybrid Algorithm for Feature Selection and Classification

<p>With a recent spread of intelligent information systems, massive data collections with a lot of repeated and unintentional, unwanted interference oriented data are gathered and a huge feature set are being operated. Higher dimensional inputs, on the other hand, contain more correlated variables, which might have a negative impact on model performance. In our model a Hybrid method of selecting feature was developed by combining Binary Gravitational Search Particle Swarm Optimization (HBGSPSO) method with an Enhanced Convolution Neural Network Bidirectional Long Short Term Memory (ECNN-BiLSTM). In our proposed system, the Bidirectional Long Short Term Memory (BiLSTM) is introduced which extracts the hidden dynamic data and utilizes the memory cells to think of long-term historical data after the convolution process. In this paper, thirteen well-defined datasets are used from the machine learning database of UC Irvine to evaluate the efficiency of the proposed system. The experiments are conducted using K Nearest Neighbor (KNN) and Decision Tree (DT) which are used as classifiers to evaluate the outcome of selected features. The outcomes are contrasted and compared with the bio-enlivened calculations like Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), and Optimization protocol using Particle Swarm Optimization (PSO).</p> <p> </p>

An Energy Efficient and Cost Reduction based Hybridization Scheme for Mobile Ad-hoc Networks (MANET) over the Internet of Things (IoT)

Wireless networks are viewed as the best-used network and specifically Portable Specially Appointed Organizations (MANETs) have tracked down numerous applications for its information transmission progressively. The plan issues in this organization are to confine the utilization of energy while communicating data and give security to the hubs. Soa protocol needs to be energy efficient to avoid network failures. Thereby this paper brings an effective energy efficient to optimize LEAR and make it energy efficient. The energy-mindfulness element is added to the LEAR guiding convention in this work using the Binary Particle Swarm Optimization method (BPSO). The recommended method selects programmes taking into account course length in addition to the programme level of energy when predicting the future. To get good results, the steered challenge is first designed using LEAR. The next step is to choose a route that enhances the weighting capability of the study hours and programming power used.This MANET has been secured using the cryptographic method known as AES.According to experimental findings, the proposed hybrid version outperformed other cutting-edge models.

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver.

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.

An Optimal SDN-Based Wavelength Allocation and Routing Method for 5G Network

Optical networks are changing as new advanced technologies emerge. With each passing year, their sizes and capabilities expand. The standard architecture for network control and management cannot handle all of these complexities. The proliferation of cloud services and the massive volume of traffic provided by content delivery networks are driving the present fast increase in Internet traffic. This obviously exacerbates congestion concerns in communication networks, with a focus on the core and backbone components in particular. Software-defined networking (SDN) is evolving into a consolidated network management system that comprises a variety of strategies aiming at network management that are based primarily on one basic principle: decoupling control plane decisions from data plane activities. An essential resource allocation strategy in an all-optical network is routing and wavelength assignment. A novel SDN-based approach is proposed to address the problem of old methods mixed with new architecture in optical networks. The network resources were optimized for optimal scheduling using a binary hybrid topology particle swarm optimization method. In terms of recovery time, blockage rate, and resource consumption, simulation results demonstrate that the suggested technique outperforms previous classical methods.

A binary particle swarm optimization approach for power system security enhancement

Improvement of power system security manages the errand of making healing move against conceivable system overloads in the framework following the events of contingencies. Generation re-dispatching is answer for the evacuation of line overloads. The issue is the minimization of different goals viz. minimization of fuel cost, minimization of line loadings and minimization of overall severity index. Binary particle swarm optimization (BPSO) method was utilized to take care of optimal power flow issue with different targets under system contingencies. The inspiration to introduce BPSO gets from the way that, in rivalry with other meta-heuristics, BPSO has demonstrated to be a champ by and large, putting a technique as a genuine alternative when one needs to take care of a complex optimization problem. The positioning is assessed utilizing fuzzy logic. Simulation Results on IEEE-14 and IEEE-30 bus systems are presented with different objectives.

ECLP: Friend Recommendation Using Ensemble Approach for Detecting Communities Performing Link Prediction

Social networks provide a variety of online services that play an important role in new connections among members to share their favorite media, document, and opinions. For each member, these networks should precisely recommend (predict) the link of members with the highest common interests. Because of the huge volume of users with different types of information, these networks encounter challenges such as dispersion and accuracy of link prediction. Moreover, networks with numerous users have the problem of computational and time complexity. These problems are caused because all the network nodes contribute to calculations of link prediction and friend suggestions. In order to overcome these drawbacks, this paper presents a new link prediction scheme containing three phases to combine local and global network information. In the proposed manner, dense communities with overlap are first detected based on the ensemble node perception method which leads to more relevant nodes and contributes to the link prediction and speeds up the algorithm. Then, these communities are optimized by applying the binary particle swarm optimization method for merging the close clusters. It maximizes the average clustering coefficient (ACC) of the whole network which results in an accurate and precise prediction. In the last phase, relative links are predicted by Adamic/Adar similarity index for each node. The proposed method is applied to Astro-ph, Blogs, CiteSeer, Cora, and WebKB datasets, and its performance is compared to state-of-the-art schemes in terms of several criteria. The results imply that the proposed scheme has a significant accuracy improvement on these datasets.

Coordination of Charging Plug-in Electric Vehicles (PEV) in Electric Distribution Networks to Minimize Power Losses and Voltage Deviations

Plug-in Electric Vehicle (PEV) is a vehicle with a drive in the form of an electric motor and gets its energy source from a rechargeable battery. PEV (grid-to-vehicle, or G2V) charging activity can pose a risk to the distribution system when PEV is injected into the system. Charging PEV in the distribution system will cause the demand for electrical power to increase. Without a proper and optimal charging coordination system for the many PEVs that exist in the distribution network system, the following technical problems will arise in the network such as: occurrence of unusual and unwanted peak loads in power consumption, voltage sag, transformers or conductors overload, as well as the occurrence of high power system losses in the distribution system. This study discusses the optimization of PEV charging coordination scheduling in a distribution system by considering power losses and voltage deviations. To optimize the coordination of PEV charging, this study used a metaheuristic method called the Binary Particle Swarm Optimization (BPSO) which was then compared with the Binary Gray Wolves Optimization (BGWO) method. This scheme is simulated on the 20kV Pujon Feeder distribution system in Malang, Indonesia. The initial simulation results, at the time when the most loads were presented, showed 80 percent of PEV penetration, increase in power losses of 54.51 percent and voltage deviation of 9.82 percent. By using the BPSO method, the results showed power loss increase of 27.2 percent and voltage deviation of 7.75 percent. Meanwhile, by using the BGWO method the increase in power losses was 31.09 percent and the voltage deviation was 7.98 percent.

SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images

Materials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers' potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model's performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.

A Nonuniform Delay-Coordinate Embedding-Based Multiscale Predictor for Blast Furnace Systems

Blast furnace is the main equipment in modern ironmaking industry. To achieve hot metal of high quality, the temperature in the blast furnace rector, often indicated by the silicon content in hot metal, should be controlled within a proper range. This brief captures the multiscale features of the blast furnace system and proposes a multiscale predictor based on nonuniform delay-coordinate embedding for silicon prediction. We use the discrete binary particle swarm optimization method to optimize nonuniform embedding parameters, including embedding dimension and time lags. Then, the predictor is trained with the nonuniform embedding features under the framework of the Taylor expansion model. Some experiments on a real blast furnace are conducted to verify the superiority of the proposed method, and the results show that the nonuniform embedding model obtains better performance with lower embedding dimension and higher accuracy than the uniform embedding model, which can be seen as a great improvement of our previous work.

New binary particle swarm optimization on dummy sequence insertion method for nonlinear reduction in optical direct-detection orthogonal frequency division multiplexing system

In the paper, a novel new binary particle swarm optimization method based on dummy sequence insertion is proposed and experimentally demonstrated in the IM-DD optical orthogonal frequency division multiplexing (OOFDM) system. This technique can mitigate nonlinearity of OOFDM system without any channel side information. Experimental results demonstrate that compared to the original scheme, the improvement in the receiver sensitivity by the proposed scheme is 1.9 dB and 3.2 dB with launch powers of 2 dBm and 8 dBm, respectively, at the BER of FEC 3.8 × 10−3 after transmission over 100-km standard single-mode fiber. At a complementary cumulative distribution function of 10−4, the PAPR of OFDM signal can be reduced about 2.8 dB by using the proposed scheme, while the receiver-side hardware is the same as the origin.

An Algorithm for Optimal Placement of Voltage Sag Monitors

Voltage sags are disturbances that deserve special attention in power quality (PQ) area, given its frequent occurrences. Their constant monitoring is, therefore, essential to diagnose its causes and mitigate economic losses of electric utility customers. However, the cost of a monitoring system may be excessive if not evaluated strategically. In this context, this work presents an algorithm for the installation of PQ monitors at strategic points of electric power distribution systems in order to diagnose voltage sags. Observability area concept and binary particle swarm optimization method were used to evaluate the problem. A sensitivity analysis was also performed, in which the influence of several parameters, such as fault resistance, system loading, detection threshold, fault type, and system expansion, was evaluated. The algorithm was validated in a Brazilian distribution system and in IEEE 34-bus system. The results indicated that the algorithm was able to detect voltage sags throughout the system using monitors at few buses, reducing the cost of the monitoring system.

Reconfiguration of Distributed Network using Binary Particle Swarm Optimization Method

Reconfiguration of Distributed Network using Binary Particle Swarm Optimization Method

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

Effective feature selection determines the efficiency and accuracy of a learning process, which is essential in human activity recognition. In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. However, in some scenarios, the optimization method based on this independence hypothesis results in poor recognition performance. This article proposes a correlation-based binary particle swarm optimization method for feature selection in human activity recognition. In the proposed algorithm, the particle swarm optimization algorithm is no longer used as a black box. Meanwhile, correlation coefficients among the features are added to binary particle swarm optimization as a feature correlation factor to determine the position of particles, so that the feature with more information is more likely to be selected. The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with the highest k-nearest neighbor classifier recognition rate would be picked as the eigenvector. Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, k-nearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.

Integrated Mutation Strategy With Modified Binary PSO Algorithm for Optimal PMUs Placement

Optimal phasor measurement units (PMUs) placement refers to the strategic placement of PMUs to achieve the full observability of power systems with a minimum number of PMUs. A strategic placement is needed because of the economic or technical restriction that hinders the deployment of PMUs on every bus. A modified version of the binary particle swarm optimization method is proposed in this paper by integrating a mutation strategy and the V-shaped sigmoid function for placing the PMUs that maintains the full power system observability in the presence of zero-injection bus, single PMU loss, and PMU's channel limits while maximizing the measurement redundancy. The solution that has the highest measurement redundancy was selected as the best placement of PMUs. The use of mutation strategy and V-shaped sigmoid function in this paper improves the population diversity, thereby minimizing the chance of the particles being trapped in the local optima, consequently leading to a quality solution. In order to validate its effectiveness, the results obtained by the proposed method are compared with other published techniques to demonstrate the accuracy and validity of the proposed technique. The results of the IEEE 300-bus system show that the proposed method effectively managed to reduce the number of PMUs needed.

Solving maximum set k-covering problem by an adaptive binary particle swarm optimization method

The maximum set k-covering problem (MKCP) consists in selecting a subset of k columns from a given set of n columns, in such a way that the number of rows covered by the selected columns is maximized. The problem is NP-hard and has lots of applications. In this paper, we propose an adaptive particle swarm optimization for solving the maximum set k-covering problem. The proposed algorithm uses a greedy constructive procedure to generate an initial swarm with good quality solutions. Based on the characteristic of the MKCP, an iterative local search procedure is developed to enhance the solution quality. Furthermore, a position updating procedure and a mutation procedure with adaptive mutation strength are employed to guide the search to a more promising area. These strategies achieve a good tradeoff between exploitation and exploration. Extensive evaluations on a set of benchmark instances show that the proposed algorithm performs significantly better than the existing heuristic for MKCP. In particular, it yields improved lower bounds for 96 out of 150 instances, and attains the previous best known results for remaining 54 instances. The key features of the proposed algorithm are analyzed to shed light on their influences on the performance of the proposed algorithm.

Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization

The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization (MO-NMBCSO) is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method (MO-BPSO). A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics.

Optimum topological design of negative permeability dielectric metamaterial using a new binary particle swarm algorithm

This paper presents a Particle Swarm Optimization-based topology optimization method for the design of negative permeability dielectric metamaterials.As the electromagnetic metamaterials have some physical properties not available in nature, they have attracted a huge scientific research interest for decades. In fact, electromagnetic metamaterials can exhibit simultaneously negative permeability and negative permittivity. The aim of this work is to find an optimal topology of a dielectric metamaterial that achieves negative permeability at a given frequency. A binary Particle Swarm Optimization is developed and applied to a negative permeability dielectric metamaterial topology design problem. The optimization process is achieved using a developed numerical model of the studied metamaterial, which is solved by the Finite Element Method.First, the governing equations and the weak formulation of the electromagnetic problem are presented. Then, the optimization problem to be solved is formulated. The developed binary Particle Swarm Optimization method, and the developed interfacing method are explained. Some numerical examples are presented to demonstrate that the binary Particle Swarm Optimization is adapted to the topology optimization of negative permeability dielectric metamaterials, at given frequencies, to demonstrate the utility and validity of the presented method.