Optimization PSO Algorithm Research Articles

Comparison of GA, ACO algorithm, and PSO algorithm for path optimization on free-form surfaces using coordinate measuring machines

Coordinate measuring machines (CMMs) play an important role in modern manufacturing and inspection technologies. However, the inspection process of a CMM is recognized as time-consuming work. The low efficiency of coordinate measuring machines has given rise to new inspection strategies and methods, including path optimization. This study describes the optimization of an inspection path on free-form surfaces using three different algorithms: an ant colony optimization algorithm, a genetic algorithm, and a particle swarm optimization algorithm. The optimized sequence of sampling points is obtained in MATLAB R2020b software and tested on a Leitz Reference HP Bridge Type Coordinate Measuring Machine produced by HEXAGON. This study compares the performance of the three algorithms in theoretical and practical conditions. The results demonstrate that the use of the three algorithms can result in a collision-free path being found automatically and reduce the inspection time. However, owing to the different optimization methodologies, the optimized processes and optimized times of the three algorithms, as well as the optimized paths, are different. The results indicate that the ant colony algorithm has better performance for the path optimization of free-form surfaces.

Application of Network Security Event Correlation Analysis and Situation Evaluation Technology

Network security situational awareness can integrate all aspects of network security elements. Through correlation analysis, information fusion, situation prediction and other technologies to realize the intelligent analysis and comprehensive decision-making of complex information systems, network security situation awareness can improve the management efficiency and effect of complex networks. In order to solve the problem of parameter optimization of existing situation assessment methods, the parameters of SVM model are optimized based on Particle Swarm Optimization PSO algorithm. This paper presents a network security situation assessment method based on PSO and SVM. Using this algorithm can get a better balance between time-consuming and improving accuracy. At the same time, the index weight is determined according to grey correlation analysis, and the training samples are input to support vector machine for training. In this paper, the improved particle swarm optimization algorithm is used to optimize the parameters of support vector machine to improve the effect of situation assessment. Simulation test results show that the evaluation method improves the effectiveness and accuracy of situation assessment.

Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost.

A single-branch impedance compression network (icn) optimized by particle swarm optimization algorithm for rf energy harvesting system

Most previous works regarding the impedance compression network ICNs reported in the literature consist of dual branches to carry out the compression functionalities. Although this type of circuits are efficient, they are complex and have large sizes. This work proposes a complex impedance compression network with only a single branch for wireless power transfer (WPT) circuits, operating at 2.4GHz under a wide input power range and a variable load. The design size is about 4x4.2mm2 which thereby, produces a simple, and small design. The simulation results demonstrate that the improvement in efficiency is by 10%, as compared to a design without ICN. The improvement can increased by utilizing the particle swarm optimization PSO algorithm where the best dimensions of the ICN components will be selected relying on the suitable fitness function in the PSO algorithm to the given desired goals. The impedance matching is also enhanced in which it stands behind the conversion efficiency increment. The circuit size is very slightly increased and it becomes 4.2x4.3mm2. The final design has a conversion efficiency about 76%, with more than 15% improvement to a design with only a matching circuit or without using the automation techniques. Eventually, results show that the enhancement is obtained not only the designated input power but also along a range of the input power.

Nonlinear hysteretic modeling and parameter identification of steel strip winding reinforced composite pipe

Steel strip winding reinforced composite pipe (SSRCP) is often used for oil and gas transportation in shallow sea. Its hysteresis characteristics under the action of low cycle load can effectively absorb external energy, which is beneficial to ensure the stability of marine structures such as ships and drilling platforms. In this paper, considering the nonlinear contact between layers, a finite element model is established to analyze the nonlinear hysteretic characteristics of SSRCP under cyclic bending load. Based on the Bouc-Wen (BW) model, an improved BW model was proposed to describe the hysteretic properties of SSRCP by introducing asymmetric factor and intensity enhancement factor. The hysteretic energy dissipation was regarded as the parameter to be identified, and the model was divided into 4 sections according to the loading law to reduce the difficulty of solving equations. Based on simulation data, PSO, GA and SA (Particle Swarm Optimization, Genetic Algorithm, and Simulated Annealing) stochastic intelligent optimization algorithm was used to identify the parameters of the improved BW model. The results show that the PSO algorithm has high calculation accuracy and efficiency, and the improved BW model can accurately describe the hysteresis characteristics of SSRCP under cyclic bending load.

Prospective hydrogen-based microgrid systems for optimal leverage via metaheuristic approaches

The paper proposes a hybrid microgrid (HMG) system with solar modules and wind turbines as primary energy sources and a diesel engine as backup. Meanwhile, the hydrogen energy storage system (HySS) is utilized as energy storage. Firstly, by reducing the levelized cost of energy (COE), this study presents the optimal size and configuration of various renewable and distributed energy resources in HMG systems. Secondly, two novel modified hybridized particle swarm optimization (PSO) and ant colony optimization (ACO) are developed, namely, ACO initialized PSO and ACO updated PSO. The optimal simulation results using PSO and ACO metaheuristic technique either individually or hybridized are compared in terms of primarily minimizing the levelized COE and achieving short execution time. Thirdly, to verify the geographical-independency of the integration of HySS for producing onsite green hydrogen locally, the estimation performance is examined for three different locations in Egypt, Mozambique and Denmark respectively using hourly meteorological and load data in 2019. Sensitivity and effectiveness analysis are carried out to highlight the major parameters that impact HySS incorporation. Finally, the system readiness level together with the different hydrogen, economy barriers constraints, limitations and risks are investigated based on the technology readiness level and the integration readiness level indices. The simulation results demonstrate the superiority of the ACO updated PSO optimization algorithm towards minimizing the levelized COE in the different regions. In the developing countries, the salvage market supports HySS integration. At system readiness levels range between 0.65 and 0.94, versatile HySS integration is verified.

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

Control of Buck-Type Dynamic Capacitor (DCAP) Using Model Predictive Control Method with PSO optimization algorithm for Dynamic Var and Harmonic Compensation

This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and harmonic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP.

A quantum-inspired particle swarm optimization for sizing optimization of truss structures

Particle swarm optimization (PSO) algorithm has some advantages such as simple principle, less model parameters. However, it has some flaws for dealing with sizing optimization of truss structures, such as low accuracy, slow astringency and poor population diversity in the later evolution stage. In order to overcome these shortcomings and deal with optimization problem of truss structures effectively, a new quantum-inspired particle swarm optimization (QPSO) algorithm with quantum angle encoding and a perturbation operator of the population is proposed in this paper. Then, QPSO is used for optimization of truss structures by means of penalty function method. In the end, two typical numerical tests for truss design are performed to calibrate and verified the proposed algorithm. The results of the QPSO compared with PSO and other optimization algorithms show that the proposed QPSO has a better performance in terms of both the optimum solutions and the convergence capability than PSO and is a powerful optimization technique for solving optimization problem of truss structures.

Application of Mathematical Model Based on Optimization Theory and Particle Swarm Algorithm in Radar Station Layout Optimization

The optimization problem of radar station layout was derived through its mathematical description to explore the application of the optimization theory and particle swarm optimization (PSO) in this optimization problem; after the optimization problem was solved, a disturbance factor was introduced to present the improved PSO algorithm (PSO-DF); in the end, the PSO-DF algorithm was used to construct an radar station layout optimization model. The simulation results of the algorithm performance and the radar station layout optimization model show that in comparison with the basic PSO algorithm, the improved PSO-DF algorithm has better local search performance; corresponding to different classical functions, the optimal value, the worst value, mean value and variance of the PSO-DF algorithm are all superior to those of the basic PSO algorithm; the fitness value corresponding to the simulation via the constructed radar station layout optimization model is about 0.8, and the network shows satisfying effects in the monitoring of different areas of responsibility. This research provides a certain reference for the application of the optimization theory and PSO algorithm in the field of radar station layout optimization.

Peningkatan Performa Algoritma CART dengan Seleksi Fitur Menggunakan ABC untuk Penilaian Kredit

Berbagai teknik statistik dan machine learning telah digunakan untuk mengembangkan model prediksi keuangan .Dalam hal ini Penilaian kredit sangat berkaitan erat dalam hal prediksi kelayakan kredit. Karena tidak ada kesepakatan umum atas rasio keuangan sebagai fitur masukan untuk pengembangan model, banyak penelitian menganggap pemilihan fitur sebagai langkah pra-pemrosesan dalam data mining sebelum membuat model.Penelitian ini menguji pengaruh seleksi fitur menggunakan Artificial Bee Colony terhadap peningkatan performa algoritma CART. Hasil eksperimental menunjukkan bahwa ABC merupakan kombinasi terbaik dari pemilihan fitur dalam meningkatkan performa algoritma CART . Dibandingkan dengan beberapa algoritma optimasi PSO dan Ant Colony metode yang diusulkan menunjukkan kinerja yang luar biasa.Dengan adanya penelitian ini diharapkan dapat menjadi sebuah rujukan dalam hal penilaian credit ,memungkinkan bank untuk menolak calon peminjam dengan kelayakan kredit yang buruk.

Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model

Deformation prediction is significant to the safety of foundation pits. Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. Combining a small amount of measured data during the excavation of a bottomless foundation pit in a Changsha subway station, the calculations based on the PSO-GM model, the PSO-BP network model, and the PSO-GM-BP model compared. The results show that both the GM (1, 1) and BP neural network models can predict accurate results. The prediction optimized by the particle swarm algorithm is more accurate and has more substantial applicability. Due to its reliable accuracy and wide application range, the PSO-GM-BP model can effectively guide the construction of foundation pits, and it also has certain reference significance for other engineering applications.

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

Robust array beamforming is a challenging task in radar, sonar and communications due to the influence of direction of arrival (DOA) mismatch and sensor position errors. However, how to enhance the robustness of beamforming is a key issue in antenna arrays. The current paper focuses on a novel approach called the improved chicken swarm optimization (ICSO) method to settle the optimization model of conventional linearly constrained minimum variance (LCMV) based on support vector machine (SVM) to against the mismatch problems as well as control the sidelobe level (SLL). As far as the ICSO method is concerned, considering that the particle swarm optimization (PSO) algorithm has outstanding convergence performance in the early iteration, the dominance of the alpha wolf in the grey wolf optimization (GWO) algorithm and the innovative mutual attraction mechanism in the firefly algorithm (FA), and we introduce these three strategies into the solution update method of conventional chicken swarm optimization (CSO) algorithm for achieving better optimization capability. Moreover, an operation of removing duplicate solutions is proposed to enhance the utilization of the population. In terms of the SVM-based LCMV beamforming algorithm, we adopt the so-called linear $\varepsilon $ - insensitive loss function to reconstruct the final cost function of LCMV by penalizing the errors between the actual and ideal array responses. Finally, we conduct simulations to evaluate the performance of the swarm intelligent optimization algorithms under an ideal scenario without mismatch and an actual scenario with the mismatch, respectively. And the results demonstrate that the developed ICSO algorithm obtains excellent robustness for different scenarios compared to PSO, FA, GWO and CSO optimization algorithms.

Solving “Limited” Task Allocation Problem for UAVs Based on Optimization Algorithms

With the rapid development of science and technology, unmanned technology has been widely used in many fields. One of the most important applications is in the field of civil and military UAVs. In the field of military UAVs (unmanned aerial vehicles), UAVs usually have to complete a series of tasks. In this series of tasks, there are often some key tasks. Key tasks play an important role, which is highly related to the feasibility of the whole action or task; mission failure sometimes causes incalculable damage. When assigning tasks to UAVs, it is necessary to ensure the accurate implementation of key tasks, so as to ensure the orderly implementation of the overall task. This paper not only successfully solved the previous problems but also comprehensively considered the minimization of resource consumption and the maximization of task revenue in the process of UAV task allocation. On the basis of considering the key system, considering the constraints and multiobjective problems in the UAV task allocation process, the violence allocation algorithm, constraint optimization evolutionary algorithm, PSO algorithm, and greedy algorithm combined with a constraint evolutionary algorithm are improved and optimized; it has been proven that they can solve the above difficulties. At the same time, several comparison experiments have been carried out; the performance and conclusion of the above four algorithms in the “limited” UAV task allocation scheme are analyzed in the experimental part.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing, power supply safety, and line loss calculations. To get accurate user-transformer relations, this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization (QPSO) and Fuzzy C-Means Clustering. The main idea is: as energy meters at different transformer areas exhibit different zero-crossing shift features, we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations. The proposed method contributes in three main ways. First, based on the fuzzy C-means clustering algorithm (FCM), the quantum particle swarm optimization (PSO) is introduced to optimize the FCM clustering center and kernel parameters. The optimized FCM algorithm can improve clustering accuracy and efficiency. Since easily falls into a local optimum, an improved PSO optimization algorithm (IQPSO) is proposed. Secondly, considering that traditional FCM cannot solve the linear inseparability problem, this article uses a FCM (KFCM) that introduces kernel functions. Combined with the IQPSO optimization algorithm used in the previous step, the IQPSO-KFCM algorithm is proposed. Simulation experiments verify the superiority of the proposed method. Finally, the proposed method is applied to transformer detection. The proposed method determines the class members of transformers and meters in the actual transformer area, and obtains results consistent with actual user-transformer relations. This fully shows that the proposed method has practical application value.

Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine

The paper constructed a depression classification model based on emotionally related eye-movement data and kernel extreme learn machine (ELM). In order to improve the classification ability of the model, we use particle swarm optimization (PSO) to optimize the model parameters (regularization coefficient C and the parameter σ in the kernel function). At the same time, in order to avoid to be caught in the local optimum and improve PSO's searching ability, we use improved chaotic PSO optimization algorithm and Gauss mutation strategy to increase PSO's particle diversity. The classification results show that the accuracy, sensitivity and specificity of classification models without parameter optimization and Gauss mutation strategy are 80.23%, 80.31% and 79.43%, respectively, while those results of classification model using improved chaotic projection model and Gauss mutation strategy are improved to 88.55%, 87.71% and 89.42%, respectively. Compared with other classification methods of depression, the proposed classification method has better performance on depression recognition.

Optimization of mooring systems in the context of an integrated design methodology

This work describes an enhanced mooring optimization procedure, oriented towards recent floating production systems (FPS) for oil & gas exploitation in ultra-deep-water scenarios, which may present a large number of risers in an asymmetric layout. Acknowledging that the risers are the key component of an FPS, the optimization procedure is associated to an integrated mooring-riser design methodology; thus, instead of simply minimizing the platform offsets and/or the costs of the mooring system itself, one of the main objectives is to obtain a mooring configuration that ensures the integrity of the risers. Other highlights of the optimization procedure include the following aspects: Enhancements in the modeling of the optimization problem (including the definition of design variables, objective function and constraints that are relevant for such actual applications); The use of the PSO optimization algorithm associated to the ε-constrained method to efficiently handle the constraints; Enhancements in the evaluation of candidate solutions, by full nonlinear time-domain dynamic Finite Element simulations with coupled models; and the implementation in a parallel computing environment to deal with the high associated computational costs. A case study considering an FPS representative of actual applications in deepwater scenarios is presented to illustrate the practical application of the optimization tool.

Presenting a Four-Lump Dynamic Kinetic Model for Methanol to Light Olefins Process Over the Hierarchical SAPO-34 Catalyst Using Power Law Models.

A four-lump dynamic kinetic model on the hierarchical SAPO-34 catalyst in the methanol to light olefins (MTO) process has been presented using the power law models. Since decreased catalyst activity in the MTO process is common, for the applicability of the proposed model, the function of catalyst activity was computed as a function of the coke percentage deposited on the catalyst. The reactant and products were divided into four lumps, including methanol and dimethyl ether (DME), light olefins (ethylene and propylene), light paraffin (methane, ethane, and propane) and heavier hydrocarbons from C4. The one-dimensional ideal plug reactor was used for the simulation of the MTO reactor. The kinetic parameters and the catalyst activity function were predicted using the particle swarm optimization (PSO) algorithm. The comparison of product distribution in the experimental model and the results of the kinetic model indicated the high accuracy of the presented model. The effect of operational parameters such as temperature and weight hourly space velocity (WHSV) on the mole percent of light olefins was investigated using the proposed kinetic model. The optimized value of temperature and WHSV to reach the maximum yield of light olefins was respectively 460 ˚C and 4.2 h-1. The passive kinetic coefficients were estimated in the reaction rate constant and catalyst activity function with the help of the PSO optimization algorithm. The mole fraction of different products and the reactant arising from modeling at the reactor outlet was compared with experimental results, which indicated the high accuracy of the presented kinetic model. The results also revealed that the selection of high and low temperatures and WHSV decreases the yield of light olefins and the lifetime of the catalyst.

A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms

This article presents a multivariable optimization of the energy and exergetic performance of a power generation system, which is integrated by a supercritical Brayton Cycle using carbon dioxide, and a Simple Organic Rankine Cycle (SORC) using toluene, with reheater (S−CO2RH−SORC), and without reheater (S−CO2NRH−SORC) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise, for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S-CO2-SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guaranteeing that it is the ideal algorithm to solve this case study.