Swarm Optimization Method Research Articles

Применение эволюционных и роевых методов для оптимизации многопараметрического управления процессом нанесения гальванического покрытия

One of the key parameters of the quality of ceramic coatings is its thickness, which must meet technical requirements and provide the necessary degree of corrosion protection, wear resistance and appearance of the coating. Ensuring the uniformity of the galvanic coating is one of the most important and complex tasks of high-tech machine-building production, for which a number of methods have been proposed, such as controlling current modes, the location of electrodes in the bath, and the electrolyte flow rate. However, in most cases, these methods require solving the problem of optimal multiparametric control. Prompt and accurate optimization when changing the conditions of electrolysis (electrode potentials, composition and properties of the electrolyte), as well as, if necessary, taking into account the multi-extreme nature of the dependence of the uniformity coefficient on the process parameters (current density, interelectrode distance, electrolyte flow rate) is a rather complex and ambiguous multifactorial task that limits the use of classical methods for- the claim of a global extreme. The article explores the possibility and expediency of using intelligent heuristic methods, such as evolutionary and swarm methods, to solve this problem. Objective. The objective of this study is to determine the effectiveness of using genetic algorithms and the particle swarm method to solve the problem of optimizing the multiparametric control of the electroplating process. Materials and methods. Methods of mathematical modeling, methods and software environment of numerical modeling and optimization were used to conduct the study. Results. The article investigates the effectiveness of the application of evolutionary and swarm optimization methods in relation to the process of applying chrome plating in a galvanic bath with many anodes with multiparametric control of current density, interelectrode distance and electrolyte flow rate. The best approximation to the extreme of the uniformity coefficient can be achieved by genetic algorithms using the mutation operation and the particle swarm method, while achieving the extreme using the particle swarm method is achieved in fewer iterations. Conclusion. The results of the study substantiate the expediency of using evolutionary and swarm methods to solve the problem of optimizing the multiparametric control of the electroplating process, while it is possible to increase the efficiency of these methods due to additional tuning.

Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF

In recent decades, swarm optimization methods have been employed to address various optimization problems in structural health monitoring (SHM). One of the widely recognized swarm-based algorithms, particle swarm optimization (PSO), has gained significant popularity and found extensive applications across diverse fields. However, it presents some limitations, such as the low convergence rate in the iterative process. The butterfly optimization algorithm (BOA) is a recently developed algorithm that has demonstrated its performance in solving a variety of optimization problems. In this research, a novel hybrid swarm optimization algorithm is introduced, integrating PSO and BOA, with the aim of enhancing its effectiveness. To overcome the limitations of the traditional Artificial Neural Network (ANN) technique and enhance its training performance, this new hybrid algorithm is integrated with ANN. The study offers valuable insights into the creation of a predictive model, known as PSO-BOA-ANN, for detecting structural damage. Input parameters for the model include natural frequencies, while the output parameter is the severity of the damage. To test the efficiency of the proposed technique, data were collected from a finite element model using a simulation tool, and from frequency response function (FRF) after experimental modal analysis for single and double cracked aluminum beams considering different crack depths. A comparative analysis was conducted between the results obtained from PSO, BOA, GA, and their respective combinations with ANN. The findings indicate that the novel PSO-BOA-ANN approach outperforms the other approaches in terms of accuracy when it comes to damage prediction.

An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application

AbstractPower density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land‐related cost” and “module‐related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m2) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver.

Control and optimization of a smart antenna array by PSO

This paper describes an array of eight Yagi–Uda antennas, a beamforming circuit (BFC) and an antenna control system (ACS), which form a switched smart antenna structure, the principal feature of which is that its main lobe is maximized using the particle swarm optimization (PSO) method, while its side lobes are minimized. The application of the PSO integrated with the 3D electromagnetic simulation software Ansys HFSS has achieved excellent results, and has been validated in practical experiments. The results measured in the open field are in agreement with the simulations and are comparable with the arrangement of the energy distribution concessionaires. As expected, the gains obtained proved to be greater when the main lobes were pointed toward the objective angles. These characteristics meet the needs of supervisory control systems used by electricity distribution companies, which require high-gain RF communication networks in order to monitor electric switches in a distant area covered by the respective base transceiver stations. The entire setup herein described and tested has performed extremely well, and is certainly a promising approach by which to control the outstations of electricity distribution companies without the need to install additional repeaters.

Overseas Warehouse Location of Cross-Border E-Commerce Based on Particle Swarm Optimization

In order to solve the problem of cross-border e-commerce warehouses as transit stations and direct selling platforms, the location of a cross-border e-commerce overseas warehouse is deeply studied on the basis of particle swarm optimization. Firstly, it studies the principle of algorithm optimization and algorithm method of particle swarm optimization. Among them, the self-built overseas warehouse mode has high requirements on the construction threshold, which is suitable for large cross-border e-commerce enterprises. The overseas warehouse model is built by a professional third party, which is economical and flexible, so it is suitable for mass cross-border e-commerce enterprises. Another overseas warehouse model is to build a cross-border logistics one-stop service platform with overseas warehouse as the core for all kinds of cross-border e-commerce enterprises, which is the development direction of overseas warehouse in the future. However, due to the immature construction conditions and too strong resource integration, this model is still in the stage of research, exploration, and attempt. However, choosing the location of an overseas warehouse and how to use the particle swarm optimization algorithm in the overseas warehouse development experiment remain problems to be solved. After experiments and research, the particle swarm optimization algorithm is used to solve the problem in the context of cross-border e-commerce, which verifies the feasibility of the model, so as to give a specific scheme for the location of overseas hub warehouses and overseas warehouses.

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

The sequence of reservoir operations has a profound influence on the regulation and storage capacity of reservoir groups to effectively utilise the natural water inflow and external water transfer in the basin, especially for reservoir groups with water supply tasks. This study establishes the reservoir operation sequence (ROS) of four reservoir group modes, aiming at national economic and ecological water consumption, constructs a model of ROS-based multi-objective ecological operation of the reservoir group, and uses the particle swarm optimisation (PSO) method to optimise the solution. Analysing the results of the three schemes in two scenarios at the Yinma River Basin (YRB) indicates that after the Central Jilin Water Supply Project is put into operation, not only will the production and living water be effectively improved, but also the ecological water in the basin. Then, we compared the optimisation results of different water supply sequences in series and parallel reservoirs, which illustrates that the ROS of the four modes formulated in this research is the optimal water supply sequence.

Informatization of National Public Service Fitness in Constructing a Smart City Using Big Data

The public service system of national fitness is the condition and guarantee for the public to participate in various fitness activities. In order to effectively integrate the existing national fitness service resources and to improve the efficiency of the public service supply, this paper aims to study the informatization of building national fitness public services in smart city under the background of big data. Firstly, we build a public service information platform of national fitness in the smart city, consisting of different modules, and then describe each module in detail. Secondly, we consider different types of fitness projects and establish the database structure model of network resources. Thirdly, we obtain the multitree cascade system of the national fitness and the heterogeneous data model of the national fitness benefit index by using high-dimensional statistical analysis method. Combined with the national fitness behavior, data mining, and support vector machine (SVM), the objective function of data mining statistical decision-making is established. Finally, the well-known particle swarm optimization (PSO) method is used to optimize the target parameters so as to realize the national fitness, big data mining, and feature analysis. The simulation results show that the proposed model has superior performance, as it can realize heterogeneous data mining of the national fitness benefit index. In addition, we quantitatively analyze the promoting effect of the national fitness on the physical quality and health level of the public.

PSO-VFA: A Hybrid Intelligent Algorithm for Coverage Optimization of UAV-Mounted Base Stations

<p>When the number of outdoor wireless users surges and fixed base stations (BSs) can hardly accommodate high-load communication traffic, unmanned aerial vehicles (UAVs) carrying BSs can provide wireless communication services, and the location deployment of the UAV-mounted BSs directly influences the reliability of network communications. For the target area scenario where the UAVs uniformly cover user nodes, we propose a hybrid intelligent coverage algorithm called PSO-VFA to optimize the coverage of a fixed number of UAV-BSs. The PSO-VFA algorithm consists of two phases employing different intelligent algorithms. First, we adopt a particle swarm optimization (PSO) method for a global search of the coverage areas. Then, for local search, a virtual-repulsive-force-based firefly algorithm (VFA) is proposed in this paper to maximize the user coverage. In the VFA algorithm, the users are treated as the objects attracting the UAVs, and the virtual repulsive force is used for UAV location adjustment. Simulation results show that the proposed PSO-VFA hybrid algorithm has faster convergence and significantly increases the communication coverage of UAV-mounted BSs compared with individual intelligent algorithms such as VFA, PSO, genetic algorithm (GA), and simulated annealing (SA).</p> <p> </p>

Statistical Analysis on Random Matrices of Echo State Network in PEMFC System’s Lifetime Prediction

The data-driven method of echo state network (ESN) has been successfully used in the proton exchange membrane fuel cell (PEMFC) system’s lifetime prediction area. Nevertheless, the uncertainties of the randomly generated input and internal weight matrices in ESN have not been reported yet. In view of this, an ensemble ESN structure is proposed in this paper to analyze the effects of random matrices from a statistical point of view. For each ESN, the particle swarm optimization (PSO) method is utilized to optimize the hyperparameters of the leaking rate, spectral radius, and regularization coefficient. The statistical results of each ensemble ESN are analyzed from 100 repeated tests whose weight matrices are generated randomly. The mean value of the ensemble ESN and a confidence interval (CI) of 95% are given during the long-term lifetime prediction. The effects of two different distribution shapes, i.e., uniform distribution and Gaussian distribution, are fully compared. Finally, the effects of the ensemble structure and two different distribution shapes are tested by three experimental datasets under steady-state, quasi-dynamic, and full dynamic operating conditions.

Study on Minimum Miscibility Pressure of CO2–Oil System Based on Gaussian Process Regression and Particle Swarm Optimization Model

Abstract Injecting CO2 into the reservoir can not only improve crude oil recovery but also achieve the goal of CO2 geological storage. It can not only reduce the greenhouse effect but also obtain additional economic benefits. From the perspective of minimum miscible pressure, carbon dioxide flooding can be divided into miscible flooding and immiscible flooding, and miscible flooding is widely used in the oil field. The most important condition for miscible flooding is to achieve the minimum miscible pressure (MMP). In the study, combining the particle swarm optimization (PSO) with Gaussian process regression (GPR), a novel intelligent GPR and particle swarm optimization (GPR-PSO) method was proposed to establish the model of predicting the MMP of the CO2 and oil system. The model uses the database with more data than in the previous literature, with 365 data points, and the value range of the data is also wider. Moreover, the accuracy of GPR-PSO model was evaluated by statistical error and graph error and compared with the prediction results of existing models. The results show that compared with other models, the GPR-PSO model has higher accuracy and wider application range, the mean absolute relative error is only 1.66%. Meanwhile, the reliability of the model is verified by the sensitivity analysis of parameters. The results show that the most influential parameter on the prediction results is the reservoir temperature, and the least influential parameter is the critical temperature of injected gas. The GPR-PSO model can be used not only to predict the MMP of CO2 and oil system but also to predict the MMP of other gases and crude oil system.

Line heating process parameter forecasting method based on optimization concept

In ship construction, line heating is a necessary process for forming ship plates with complex curvature. The determination of line heating process parameters has a significant impact on the accuracy of the process and the efficiency of shipbuilding. However, previous studies on this subject only stay at the level of predicting deformation, which has not meet the actual processing production requirements. In this paper, a new idea of forecasting line heating parameters proposed. Firstly, an Radial Basis Function (RBF) proxy model between processing parameters and deformation was constructed based on experimental data, and then the reliability of the proxy model is verified. The optimization objective for proxy model was set as the minimum difference between the final deformation and the target deformation. Meanwhile, an improved Multi-Objective Particle Swarm Optimization (MOPSO) method was employed to search the optimal-set of processing parameters with non-dominated solutions. The performance of the optimized parameter set was ranked by using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and the processing parameters combination ranking first was selected as the parameter forecasting result. The results showed that the predicted processing parameters have some reference for the practical engineering.

An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants

Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers.

Biparametric identification for a free boundary of ductal carcinoma in situ

In this paper we investigate an inverse problem of two parameter identification with free boundary conditions modeling ductal carcinoma in situ (DCIS). Based on the characteristics of the DCIS model, we present an inverse problem of ductal carcinoma in situ (IPDCIS) under the conditions of incisional biopsy measurements at two different moments. Compared with the data in other literatures, this kind of measurements are more feasible and easy to obtain. Moreover, the uniqueness solution to the IPDCIS is proved. The IPDCIS of simultaneously determining unknown parameter and boundary function is transformed into a optimization problem, which can be solved by particle swarm optimization (PSO) method. The numerical simulation results are included to demonstrate the validity of the method and accuracy of the formulation of the IPDCIS. According to the information of clinical incision biopsy, the mathematical model of incision diagnosis of tumor growth pattern is established, and the unknown coefficients in the model are determined based on the proposed mathematical model.

Multi-Objective Feeder Reconfiguration Using Discrete Particle Swarm Optimization

Electric power distribution systems have been heavily engaged in evolutionary changes toward effective usage of distribution networks for dependability, quality, and improvement of services delivered to customers throughout the years. This was accomplished via a procedure known as reconfiguration. Several strategies have been offered by various authors for successful distribution feeder reconfiguration with a novel optimization method. As a result, this work developed a Discrete Particle Swarm Optimization (DPSO) method to address the issue of distribution system feeder reconfiguration during both steady-state and dynamic power system operations. In a dynamic state, the power demand and generation required are continually changing over time, and the DPSO algorithm finds a new set of solutions to fulfill the power demand. Many network topologies are investigated for the dynamic operation. The feeder reconfiguration single-objective optimization problem was transformed into a multi-objective optimization problem by taking into account both real power loss reduction and distribution system load balancing. The suggested technique was verified using various IEEE 16, 33, and 69 bus standard test distribution systems to determine the efficiency of the developed DPSO algorithm. The simulation findings reveal that DPSO outperforms other optimization algorithms in terms of actual power loss reduction and load balancing, while solving multi-objective distribution system feeder reconfiguration.

Swarm Optimization and Machine Learning for Android Malware Detection

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms are evaluated using well-known Machine Learning (ML) classifiers such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF), K–Nearest Neighbor (KNN) & Support Vector Machine (SVM). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The experimental results yielded an accuracy of 98.87% with just seven features out of hundred API call features, i.e., a massive 93% of data optimization.

Design and Optimization of Infinite Impulse Response Full-Band Digital Differentiator Using Evolutionary and Swarm Intelligence Algorithms

In this paper, the design and optimization of infinite impulse response full-band digital differentiator (DD) using evolutionary and swarm intelligence algorithms is investigated. Different objective functions based on the absolute error, the squared absolute error and the min-max optimality criterion are investigated. The optimal DD parameters that result in the best minimum value of the investigated objective functions are obtained using differential evolution, particle swarm optimization, genetic algorithm and cuckoo search optimization methods. These algorithms are used due to their simplicity, efficiency and robustness in solving general multidimensional optimization problems. The investigation outcomes show that minimizing the absolute error gives the most flat magnitude response, and minimizing the squared absolute error gives almost the lowest mean error of the designed DD. In addition, a new objective function is proposed to improve the linearity of the phase response of the designed infinite impulse response full-band DD. It is found that the design of the DD using the differential evolution outperforms or at least is comparable to similar designs reported in the literature using other optimization methods.

State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this article examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian information criterion (BIC). Based on the correlation between capacity and resistance, this article concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multibattery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

Metode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review

In the era of the industrial revolution 4.0 as it is today, where the internet is a necessity for people to live their daily lives. The high intensity of internet use in the community, it causes the distribution of information in it to spread widely and quickly. The rapid distribution of information on the internet is also in line with the growing growth of digital data, so that the public opinions contained therein become important things. Because, from this digital data, it can be processed with sentiment analysis in order to obtain useful information about issues that are developing in the community or to find out public opinion on a company's product. The number of studies related to sentiment analysis that applies the Naive Bayes algorithm to solve the problem, so researchers are interested in conducting research on the use of feature selection for the algorithm. Therefore, this research was conducted to determine what feature selection is the most optimal when combined with the Naive Bayes algorithm using the Systematic Literature Review (SLR) research method. The results of this study concluded that the most optimal feature selection method when combined with the Naive Bayes algorithm is the Particle Swarm Optimization (PSO) method with an average accuracy value of 89.08%.

DYNAMIC MODELLING AND OPTIMAL DESIGN OF BUILDINGS WITH FRICTION DAMPERS USING PARTICLE SWARM OPTIMIZATION

Formulation of the problem. Dry friction dampers are widely used for the protection of buildings from dynamic and seismic loads. Their advantages are the simplicity and reliability of the design, low costs, easy installation and maintenance, as well as high dissipative characteristics. Determining the location of friction dampers inside a building is a complex task that requires a comprehensive analysis of the dynamic properties of the structure and, as a rule, cannot be solved within the framework of standard design methods. Therefore, a crucial problem is the development of novel methods for calculating structures with dry friction dampers allowing finding optimal design solutions to minimize dynamic and seismic impacts.
 The purpose of the sudy. The method of particle swarm optimization is developed for the prediction of rational locations of friction dampers in multi-storey frame buildings to enhance their resistance to seismic loads. 
 Conclusions. An analytical model describing the dynamic behavior of buildings with friction dampers is proposed. A mathematical model of seismic loads is developed. Non-stationary vibrations of a building under the seismic loads are investigated. The governing non-linear dynamical equations are integrated numerically by the Runge-Kutta method. The rational locations of friction dampers are determined ensuring the minimal inter-storey drifts and accelerations of the building. The obtained results can be used in the design of houses and structures with enhanced resistance to seismic and dynamic impacts.

A PSO Optimal Power Flow (OPF) Method for Autonomous Power Systems Interconnected with HVDC Technology

High-voltage direct current (HVDC) technology has been the state of the art in power systems’ interconnections, especially for submarine installations. Multi-terminal HVDC networks and furthermore mixed AC–DC networks demand new operational modes. Techniques can be employed for the optimal operation of the whole interconnected system. In this article, an optimal power flow (OPF) technique is proposed for the steady state operation of such systems, which could be very useful in planning or scheduling of AC–DC networks. Particle swarm optimization (PSO) method has been employed to minimize the operational cost of the system. This cost includes fuel cost and investment for new transmission lines, while power losses are limited. This method-technique had been tested on real power AC power systems that are planned to be interconnected with HVDC systems. A detailed model of the simulated system was developed, and specific operational scenarios were examined.