Binary Particle Swarm Optimization Research Articles

AGGREGATION AND HYBRID APPROACHES USING BPSO AND BGWO FOR FEATURE SELECTION FOR INDIAN STOCK INDEX PRICE PREDICTION USING GRU

The stock exchange is a significant component of the economy, and forecasting its development is essential. Several deep-learning time series models based on RNN and its variants are used to forecast the stock market, but their accuracy still needs to be improved. Optimization strategies, such as GA, PSO, GWO, and others, have been used to enhance the reliability of these models. In this proposed paper, we have used the Binary GWO and Binary PSO algorithms to optimize the input characteristics of a two-layer GRU model for forecasting Indian stock index price. To increase the accuracy of these models, we have suggested two distinct approaches: Aggregation Method and the Hybrid method. In the 10- year time frame, the data indicated that the aggregation approach produced superior accuracy than standard Binary GWO and Binary PSO independently. Although the suggested hybrid method did not out perform individual Binary PSO methods in terms of accuracy, it performed better than the Binary GWO model in a 10- year time frame. Moreover, it showed promise for future improvement. The proposed approaches have the potential to make essential contributions to the index of stock price forecasting, particularly in the Indian market.

Binary Restructuring Particle Swarm Optimization and Its Application.

Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.

Two phased Yarn scheduling with novel binary hypergraphed PSO

Two phased Yarn scheduling with novel binary hypergraphed PSO

Feature selection for domain adaptation using complexity measures and swarm intelligence

Particle Swarm Optimization is an optimization algorithm that mimics the behaviour of a flock of birds, setting multiple particles that explore the search space guided by a fitness function in order to find the best possible solution. We apply the Sticky Binary Particle Swarm Optimization algorithm to perform feature selection for domain adaptation, a specific type of transfer learning in which the source and the target domain have a common feature space, a common task, but different distributions. When applying Particle Swarm Optimization, classification error is usually employed in the fitness function to evaluate the goodness of subsets of features. In this paper, we aim to compare this approach with using complexity metrics instead, under the assumption that reducing the complexity of the problem will lead to results that are independent from the classifier used for testing while being less computationally demanding. Therefore, we carried out experiments to compare the performance of both approaches in terms of classification accuracy, speed and number of features selected. We found out that our proposal, although in some cases incurs in a slight degradation of classification performance, it is indeed faster and selects fewer features, making it a feasible trade-off.

Damage-based design of multiple tuned mass dampers to improve the seismic performance of steel frame structures

Tuned mass dampers are robust tools that can control the undesired vibrations induced by wind and earthquakes. Although vibration control systems such as tuned mass dampers have been used many times by researchers to reduce roof displacement, story drift ratio, and roof acceleration, no research has been reported regarding the reduction of damage's value in the structure. Therefore, in this paper, a three-bay ten-story steel frame building is modeled using the OpenSees software. The building is excited by an earthquake. The Park-Ang damage index is utilized here as an objective function to determine the optimum TMD numbers and parameters. The optimal numbers and properties of TMD systems are calculated by a hybrid binary and real-coded particle swarm optimization (PSO) algorithm. The results indicate the ability of multiple tuned mass dampers to reduce the overall damage and damage stories' values. The results show that this objective function also decreases the maximum drift ratio, residual displacements, and roof displacements. It will lead to improving the seismic performance of the structure. Also, the results show that the damage values are distributed uniformly along with the structure's height by selecting the appropriate domain for the value of the story's damage. A comparison between the proposed method and the Den-Hartog method shows that the proposed method's ability to improve the structure's seismic performance is greater than the classical methods. It can also be concluded from the results that by optimally adjusting the parameters of the control system, the residual displacement of the roof and the displacement of the roof have been reduced by 68.91% and 66.01%, respectively.

Transmission expansion planning considering solar photovoltaic using novel binary particle swarm optimization

Due to the demand growth and an increase in uncertain generation from solar photovoltaics which are promoted instead of fossil fuel, the transmission expansion planner needs to take this uncertain generation into account. This paper proposes Transmission Expansion Planning (TEP) considering solar PhotoVoltaic (PV) using Novel Binary Particle Swarm Optimization (NBPSO) algorithm to define the location and number of the Transmission Lines Candidate (TLC) that will be installed into the existing power system. The goal is to minimize the total cost consisting of the investment cost of the newly installed TL and the operation cost of power generation. The case study consists of two cases. The TEP when solar PV is disregarded is solved and analyzed in the first case. For the second case, the TEP when considering solar PV generation is studied. The aforementioned cases were analyzed on the IEEE 24 bus test system. The optimization program is written by MATLAB software with the MATPOWER tool to solve optimal power flow. From the simulation result, the NBPSO can find the TEP solution with the lowest investment cost and operating cost while the obtained results do not violate the power system constraints.

Collaborative Planning for Stacking and Installation of Prefabricated Building Components Regarding Crane-Collision Avoidance

Site stacking of the prefabricated building components (PBC) should be planned in coordination with installation schedule and other dynamic constraints to improve the overall construction efficiency. This study explores the collaborative planning for stacking and installation of the PBCs (CPSIP) by avoiding collision of the tower cranes that are in the overlapping area and operated simultaneously with the objective of minimizing the prefabricated construction duration. The CPSIP problem, crane-collision cases, collision-avoiding strategy, and multiphase dynamic optimization strategy are analyzed. Then the optimization model for the CPSIP problem subject to the crane-collision avoiding and other dynamic constraints is proposed, and the binary particle swarm optimization (PSO) algorithm is utilized to solve the optimization model for the CPSIP problem. A case study is presented to demonstrate and justify the developed method for obtaining the CPSIP. This study contributes to the knowledge and methodology for achieving the CPSIP by avoiding crane-collision, thus improving efficiency of the prefabricated construction while promoting safety.

Benchmarking of Heuristic Algorithms for Energy Router-Based Packetized Energy Management in Smart Homes

This article presents an ER-based PEM strategy for PV integrated smart homes to jointly optimize their load scheduling delays, energy transactions cost, and battery degradation cost. The proposed approach incorporates a MA case, where, the ER acts as a main selecting agent realized by all other system elements. This leads to a combinatorial optimization problem, which can be effectively solved by heuristic optimization methods (HOMs), namely, genetic algorithm (GA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and harmony search algorithm (HSA). Specifically, we investigate the impact of the hyperparameters of the HOMs on the designed ER-based PEM system. Simulations are carried out for multiple smart homes under varying weather conditions to evaluate the effectiveness of HOMs in terms of selected performance metrics. Results show that the ER-based PEM reduces the average aggregated system cost, ensures economic benefits by selling surplus energy, while meeting customers energy packet demand, satisfying their quality-of-service, and operational constraints.

Resource Allocation for 5G Network Considering Privacy Protection in Edge Computing Environment

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the “Internet of Everything” and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers.

Research on EEG Signal Emotion Recognition Based on BPSO

The discrete binary particle swarm optimization algorithm proposed in this work can address the difficulty of imprecise emotion-related typical features and localization of emotion recognition. Eighteen classes of features in the time domain, frequency domain and time-frequency domain and differential entropy features are used as features for holistic emotion recognition accuracy using linear and nonlinear methods. Then, the next particle position, velocity and fitness are updated according to the recognition rate accuracy, so as to obtain the optimal feature combination by the emotion recognition accuracy, and lastly the subjects were identified using SVM to get the emotion recognition outcome. The initial experiments reveal that the percentage of correctness was 88.95% and 76.12% for BPSO and PSO, respectively. and the highest correct rate of happy among the four categories of emotions is 91.78%, which is improved by up to 12.83% using the BPSO algorithm.

Effective Feature Selection Strategy for Supervised Classification based on an Improved Binary Aquila Optimization Algorithm

Feature Selection (FS) is considered a crucial step in machine learning and data mining tasks, which facilitates minimizing the direct consequence of redundant and irrelevant attributes on the model’s accuracy. Hence, the researchers developed various algorithms to choose the most appropriate features for improving the accuracy rate of the presented dataset. Nevertheless, these algorithms may fall into local optima problems when applied to substantial feature sizes’ datasets. In this paper, for handling the FS strategy through dimensionality-lessening while improving the classification accuracy, an effective Aquila Optimization (AO) algorithm is introduced. AO has stable exploration and exploitation capabilities. It is enhanced by integrating a random position amendment approach with Local Search (LS) strategy to avoid local optima and then cloned into a binary version called Improved Binary AO (IBAO). Additionally, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) are quality estimators. On 18 multi-scale benchmarks, the IBAO algorithm is compared with the original BAO algorithm and twelve recent algorithms, such as Binary Artificial Bee Colony (BABC), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Whale Optimization Algorithm (BWOA), Binary Grey Wolf Optimization (BGWO), Binary Sailfish Optimizer (BSFO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO). According to Wilcoxon’s rank-sum test (α=0.05), the dominance and significant influence of IBAO are apparent on both small and large dimensional benchmarks and obtained classification accuracy up to 100% in some of these benchmarks integrated with a feature reduction size down to 92%.

Power Loss Minimization through Reconfiguration Using BPSO-Based Technique

Power loss is influenced by uncertain and ever increasing loads in the system but can be minimized by network reconfiguration where the topological structure of the system is changed by altering the switching operation. In this work, a multi-objective framework through reconfiguration is formulated with loss minimization as primary objective and reliability and voltage profile improvement as secondary objectives. A binary particle swarm optimization (BPSO) base method is developed to reconfigure the network by fulfilling all distribution network constraints. The influence of the BPSO parameters to obtain the optimal switches with minimized power loss, reliability indices, and voltage deviations are studied by a faster convergence. The results have been compared for different weights of the objective to give a comprehensive understanding of the nature of the objective attempted in this work. IEEE 33 bus and radial IEEE 69 bus systems were considered for testing the proposed method.

Multi-objective particle swarm optimization for key quality feature selection in complex manufacturing processes

In this paper, a feature selection (FS) method is proposed to identify key quality features (KQFs) in complex manufacturing processes. We propose a multi-objective binary particle swarm optimization algorithm, called MPBPSO, with three new components to optimize a bi-objective FS model of maximizing the geometric mean (GM) measure and minimizing the number of selected features. First, MPBPSO uses a modified probability-based solution update (PSU) mechanism which utilizes a flipping vector to update particles. A mutation operator with three basic operations, i.e., add, eliminate, and interchange, is also utilized in MPBPSO to improve the exploration performance. Second, a strategy combining the Pareto dominance concept with a distance measure is proposed for MPBPSO to update pbest (personal best position). Finally, a selection strategy based on the roulette wheel selection is proposed to determine the gbest (global best position) from the non-dominated set during iterations. The experimental results on four datasets have shown that the proposed FS method can identify a small number of KQFs that have good predictive ability for product quality. Further analysis indicates that MPBPSO obtains better search performance than eight benchmark optimization algorithms and the new components in MPBPSO are effective for improving its search performance.

An unconstrained relaxation digital‐to‐analog converter using optimal bit sequence

AbstractBased on the relaxation digital‐to‐analog converter (ReDAC) architecture and the capacitor charge/discharge function under discrete time conditions, this paper proposes a new mathematical model for the output voltage of the ReDAC to implement the unconstrained relaxation DAC (Uc‐ReDAC). The system clock calibration is not required. An adaptive probabilistic binary particle swarm optimization (AP‐BPSO) algorithm is proposed for solving the binary coding configuration problem under the Uc‐ReDAC model. Simulation results show that the 8‐bit (10‐bit) Uc‐ReDAC has an average sampling rate of 7.9 MS/s (6.76 MS/s).

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>

Assessment of most critical project delays on a multi-stage transmission expansion plan using particle swarm optimization

Transmission reinforcement projects of a Transmission Expansion Plan can suffer implementation delays that can impact the short-term power system operation in unpredicted ways. These delays may lead project managers to paying particular care to the development of those plans proved to be most critical from this point of view. This paper proposes a multi-objective dynamic-programming based method to evaluate the impacts of such delays in a short-term transmission plan with respect to load shedding, reliability and losses. It uses the Binary Pareto-Optimal Particle Swarm Optimization as optimization tool. For the trade-off analysis and the final decision to be made, the paper proposes indices to rank the projects in the plan from most to least critical, should they be delayed, based on the severity of the impacts and the probability of occurrence, both on an annual basis and for the whole planning horizon. The proposed method has been tested on an actual Brazilian 40-bus subtransmission system for a 3-year and a 6-year planning horizon, and with the 24-bus IEEE RTS reliability test system for a 3-year planning horizon. To assess its accuracy and efficiency, the results are compared to those using the Non-dominated Sorting Genetic Algorithm II method (NSGA-II) and to the exhaustive search that considers every delay configuration, showing the same accuracy as the exhaustive search and better convergence properties than NSGA-II. The most critical projects are accurately ranked helping the project manager to decide which projects should receive higher attention to guarantee its construction on the planned date. The method obtained a reduction in computational time of 35% for the Brazilian system and of 42% for the IEEE RTS in relation to the exhaustive approach, demonstrating its efficiency.

An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing.

Satellite edge computing has attracted the attention of many scholars due to its extensive coverage and low delay. Satellite edge computing research remains focused on on-orbit task scheduling. However, existing research has not considered the situation where heavily loaded satellites cannot participate in offloading. To solve this problem, this study first models the task scheduling of dynamic satellite networks as a minimization problem that considers both the weighted delay and energy consumption. In addition, a hybrid genetic binary particle swarm optimization (GABPSO) algorithm is proposed to solve this optimization problem. The simulation results demonstrate that the proposed method outperforms the other three baseline algorithms.

Research on classification method of multimodal magnetic resonance images of Alzheimer's disease based on generalized convolutional neural networks

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.

Optimizing the wind power generation cost in the Tirumala Region of India

Global warming is impacting almost every nation of the world and causing excessive socioeconomic damage to human civilization. India is presently the second most inhabited country on the planet and possesses the noteworthy potential to curb global greenhouse gas emissions. Most of the stakeholders of the global communities have signed the Paris treaty of 2015 to curtail the surface temperature rise. As the central government of India has announced its target to attain net zero-emission by the end of 2070, the electricity generation sector of the country needs to utilize renewable resources like wind energy rapidly. This paper focuses to optimize the wind energy generation cost in the Tirumala region of the country using the Genetic Algorithm and Particle Swarm intelligence concurrently. Tirumala is located in the area of Tirupati in the southern state of Andhra Pradesh. A relative analysis of the optimization outcomes validates the superiority of the Genetic Algorithm over the Binary Particle Swarm Optimization Algorithm for minimizing the wind energy generation cost. The application of the Genetic Algorithm has been proven to cut down the generation cost by up to 7.56% as compared to the usage of Binary Particle Swarm Optimization for similar terrain conditions and wind flow conditions in the Tirumala Area.

Improved binary particle swarm optimization for the deterministic security-constrained transmission network expansion planning problem

- This paper proposes an improved version of Binary Particle Swarm Optimization (BPSO) to solve the security-constrained transmission network expansion planning (TNEP) problem using the DC network model. Some modifications, such as superior population configurations, are made to improve the efficiency of the BPSO by adapting it to the needs of the TNEP to obtain better results. A parallel implementation of the improved BPSO reduces execution times. Transmission losses are included using a quadratic function and piecewise linear approximation approach. Considering transmission losses in the long-term planning horizon can avoid underinvestment in network capacity and result in a different network expansion plan. The proposed approach is validated with numerical simulations of the classic Garver system, and its performance is tested on the IEEE 24-bus and 118-bus systems for a ten-year planning horizon. The simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO.