Particle Swarm Optimization Genetic Algorithm Research Articles

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs’ surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues

Rising market demands, economic pressures, and technological advancements have spurred researchers to seek ways to enhance business environments and scientific productivity. Predictive science, crucial in this context, has gained prominence due to the rapid progress in information technology and forecasting algorithms. Time series forecasting, widely used in fields like engineering, economics, tourism, and energy, has inherent limitations with classical statistical methods, leading researchers to explore artificial intelligence and fuzzy logic for more accurate predictions. However, despite extensive efforts to improve accuracy, challenges persist. The research introduces a model aimed at surpassing existing methods in time series forecasting accuracy. This approach combines meta-heuristic optimization algorithms and neutrosophic logic to enhance precision in uncertain and complex environments, promising improved forecasting outcomes. The study shows that the performance of the neutrosophic time series modeling approach is highly dependent on the optimal selection of the universe of discourse and its corresponding intervals. This study selects the quantum optimization algorithm (QOA), genetic algorithm (GA), and particle swarm optimization (PSO) to address this weakness. These optimization algorithms improve the performance of the NTS modeling approach by selecting the global universe of discourse and corresponding intervals from the list of locally optimal solutions. The proposed hybrid model (i.e., NTS-QOA model) is verified and validated with datasets of university enrollment of Alabama (USA), Taiwan futures exchange (TAIFEX) index, and Taiwan Stock Exchange Corporation (TSEC) weighted index. Various experimental results signified the efficiency of the proposed model over existing benchmark models in terms of average forecasting error rate (AFER). This value using the proposed NTS QOA, NTS GA, and NTS PSO method on the university dataset is 0.166, 0.167, 0.164, on the TAIFEX dataset, is 0.081, 0.081, and 0.081, and on the TSEC dataset is 0.09, 0.09, and 0.09, respectively.

Comprehensive optimization framework for transmission expansion planning with fractional frequency transmission system

AbstractThe Fractional Frequency Transmission System (FFTS) operates with low frequency to decrease the electrical length of AC transmission lines, consequently enhancing transmission capacity. The integration of renewable energy sources via FFTS will significantly impact power system planning. This paper presents a Fractional Frequency Transmission Expansion Planning (FF‐TEP) model, which considers the interaction between FFTS and the industrial‐frequency power grid. This model addresses the challenges posed by the integration of offshore wind power through FFTS, and takes several key factors into account, including the active power coupling of Modular Multilevel Matrix Converter (M3C), inverter capacity, and converter transformer capacity. To address the non‐convexity caused by AC power flow, this paper introduces a Genetic‐Particle Swarm Optimization algorithm (G‐PSO) with dynamic penalties tailored to the M3C structure. This approach incorporates both crossover and mutation operators from Genetic Algorithms (GA) into Particle Swarm Optimization (PSO). The penalty function dynamically adapts according to the M3C structure, resulting in improved global optimization while maintaining computational efficiency. The results of two case studies demonstrate that FFTS can significantly enhance transmission capacity while reducing cable charging power. This reduces investment costs and decreases voltage offset issues.

Optimization of X-axis servo drive performance using PSO fuzzy control technique for double-axis dicing saw

The dicing saw is a critical piece of equipment in IC processing, primarily used to cut wafers. Due to the high spindle speed, even small errors in the cutting process can result in wafer chipping or cracking. Therefore, the dicing saw requires a high degree of accuracy and stability. In this paper, the accuracy of the X-axis servo response was simulated using an Israeli ADT-8230 dual-axis abrasive wheel dicing saw. The study introduces a novel approach by using a fuzzy controller instead of the traditional position loop proportional integral (PI) controller. In addition, a two-input, two-output fuzzy rule is used for on-line correction of the position loop PI parameters. A heuristic algorithm is used to optimise the position loop fuzzy controller parameters. The quantization and proportionality factors are rectified using Particle Swarm Optimisation (PSO) algorithm and Genetic Algorithm (GA) respectively. By comparing the performance of the PSO fuzzy and GA fuzzy controllers, the optimal control method is derived. The proposed method is validated by simulation in the MATLAB/Simulink development environment using real ADT-8230 servo data. Experimental results show that the PSO-fuzzy structured controller reduces the position control error by 11.8%, improves the tracking performance by 26% and reduces the torque pulsation by 23%. Therefore, in future research, more advanced search algorithms should be further combined to improve the servo accuracy of the dicing saw.

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Auto-setting multi-soliton temporal spacing in a fiber laser by a hybrid GA-PSO algorithm.

Multi-soliton operation in fiber lasers is a promising platform for the investigation of soliton interaction dynamics and high repetition-rate pulse. However, owing to the complex interaction process, precisely manipulating the temporal spacing of multiple solitons in a fiber laser is still challenging. Herein, we propose an automatic way to control the temporal spacing of multi-soliton operation in an ultrafast fiber laser by a hybrid genetic algorithm-particle swarm optimization (GA-PSO) algorithm. Relying on the intelligent adjustment of the electronic polarization controller (EPC), the on-demand temporal spacing of the double solitons can be effectively achieved. In particular, the harmonic mode locking with equal temporal spacing of double solitons is also obtained. Our approach provides a promising way to explore nonlinear soliton dynamics in optical systems and optimize the performance of ultrafast fiber lasers.

Enhancing daily rainfall prediction in urban areas: a comparative study of hybrid artificial intelligence models with optimization algorithms

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

A sliding mode control of the bearingless permanent magnet slice motor for the blood pump based on the GAPSO

In this paper, we proposed a sliding mode control method for the bearingless permanent magnet slice motor for the blood pump based on the genetic particle swarm algorithm, which aims to solve the problems of strong coupling, strong interference, nonlinearity and uncertainty. Firstly, the mathematical model of rotor torque and suspension force of the bearingless permanent magnet slice motor is established. Secondly, the structure of sliding mode observer is deduced by designing sliding mode surface and control law. And, the performance parameters of sliding mode observer are optimized by the genetic particle swarm optimization algorithm. Thirdly, electromagnetic torque and suspension force control under this control method is studied by Simulink. Finally, the control method is applied to the control of the blood flow of the blood pump, and the rotation speed can effectively control the blood flow. The results indicate that compared with PID control and traditional sliding mode control methods, the sliding mode control method optimized by the genetic particle swarm optimization algorithm greatly improves the control performance of bearingless permanent magnet slice motor. The results show that the blood flow can meet expectations with a small error, which fully meets the blood perfusion requirements of the blood pump.

Active Distribution Network Reactive Power Optimization Based on an Improved Genetic Particle Swarm Optimization Algorithm

As the distributed generation is gradually connected to the distribution network, it will lead to an increase in node voltage deviation and network loss. The active distribution network reactive power optimization approach based on an improved genetic particle swarm optimization algorithm is proposed in this paper to increase voltage quality and system efficiency. First, a reactive power optimization model for active distribution networks that has the least possible active power loss and voltage variation is developed. Then, the genetic idea is applied to the particle swarm optimization algorithm to improve it and address the issues of the long iteration time of the genetic algorithm and the tendency of the particle swarm optimization algorithm to settle into local optimal solutions. Finally, the enhanced genetic particle swarm optimization algorithm described in this paper is validated using the enhanced IEEE-33 node system. The findings indicate that this strategy can significantly shorten computation times while also increasing calculation precision.

A time-bounded approach for reducing runtime of well placement optimization using PSO and PSOGA algorithms

Determining the optimal drilling locations and well production/injection rates is a highly complex decision in reservoir development. The present study aims to optimize well placement and production/injection rates through the application of intelligent search algorithms. The Particle Swarm Optimization (PSO) and combined Particle Swarm Optimization-Genetic Algorithm (PSOGA) have been chosen as the optimal means to achieve this objective. The Net Present Value (NPV) of the project serves as the objective function, with decision variables including the location of wells, perforation, and flow rates. To evaluate the performance of the algorithms in optimizing well placement and flow rates, a heterogeneous reservoir model has been used. In order to reduce optimization runtime, a novel method has been presented. This method involves changing the termination condition, such that the entire optimization time is considered rather than the maximum iterations. The population size can be added to enhance algorithm convergence. Thus, modified algorithms, namely TPSO and TPSO-GA, have been introduced. The results of determining the termination condition on the TPSO-GA algorithm are quite promising. By setting a 24-h termination condition or a 70% reduction in the basic runtime, and with a population size of 30 agents, the same values of the objective function can be obtained. Furthermore, the impact of two parameters within the objective function, namely the oil price and discount rate, has also been explored.

Broadband achromatic metalens design based on predictive neural network and particle swarm optimization-genetic algorithm

The main challenge in designing broadband achromatic metalenses is to achieve the desired phase distribution at different wavelengths and positions. Finding the exact relationship between phase modulation and the size or shape of individual nanopillars is a critical but time-consuming step. This paper presents a novel joint design framework predictive neural networks and particle swarm optimization-genetic algorithms, which combines predictive neural network (PNN) and particle swarm optimization-genetic algorithm (PSO-GA). The proposed framework aims to accurately predict the phase response of nanopillars using PNN, increase the number of phase data points to establish a one-to-one correspondence between the phase and nanopillar parameters in the design of broadband achromatic metalenses, and optimize the parameters of an individual nanostructure of the metalens using PSO-GA. To validate the efficacy of the proposed method, a broadband achromatic metalens for line polarization light in the range of 260–350 nm is designed. Numerical simulations demonstrate that the designed metalens exhibits achromatic focusing. The method proposed in this paper may find wider application in the design of more complex metasurface devices.

Multidimensional Knapsack 0-1 Solution With Algorithm Evolution Pso-Ga

This paper develops the particle swarm optimization (PSO) method and uses a genetic algorithm (GA) by changing the distribution of articles in the initialization of the initial position. PSO at this time the search and speed of particles will always go to the best solution so that by narrowing the search area will be faster by updating the best position of PSO. While the Genetic algorithm plays a role to get an expanded search area for PSO solutions by utilizing crossover and mutation in GA. So that GA will expand the range of candidates for the best solution in PSO. From each of the advantages of PSO Update and GA will be combined to get Evolutionary PSO-GA (EVPGA) that can minimize error and speed up computation (itation) in finding the best solution. By using the Multidimensional Knapsack data set, the results of EVPGA get an average speed of 24.9s with an error of 1.49%.

Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks

Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.

Proposed energy efficient clustering and routing for wireless sensor network

Wireless sensor network (WSN) is considered a growing research field that includes numerous sensor nodes used to gather, process, and broadcast information. Energy efficiency is considered one of the challenging tasks in the WSN. The clustering and routing are considered capable approaches to solve the issues of energy efficiency and enhance the network’s lifetime. In this research, the multi-objective-energy based black widow optimization algorithm (M-EBWOA) is proposed to perform the cluster-based routing over the WSN. The M-EBWOA-based optimal cluster head discovery is used to assure an energy-aware routing over the WSN. The main goal of this M-EBWOA is to minimize the energy consumed by the nodes while improving the data delivery of the WSN. The performance of the M-EBWOA is analyzed as alive and dead nodes, dissipated energy, packets sent to base station, and life expectancy. The existing research such as low-energy adaptive clustering hierarchy (LEACH), hybrid grey wolf optimizer-based sunflower optimization (HGWSFO), genetic algorithm-particle swarm optimization (GA-PSO), and energy-centric multi-objective Salp Swarm algorithm (ECMOSSA) are used to evaluate the efficiency of M-EBWOA. The alive nodes of the M-EBWOA are 100 for 2,500 rounds, which is higher than the LEACH, HGWSFO, GA-PSO, and ECMOSSA.

Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension

In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.

Buckling resistance prediction of high-strength steel columns using Metaheuristic-trained Artificial Neural Networks

The buckling behavior of columns, as the most influential members regarding the stability of structures, has been a long-standing field of interest. Moreover, due to the conservative attitude of classical theories, the ultimate buckling load obtained through these recommendations is mainly considered an underestimation of its actual value. This paper hence aims to develop practical metaheuristic-trained Artificial Neural Networks to predict the ultimate buckling load of High Strength Steel columns. Therefore, initially, the ABAQUS Finite Element Model was verified with experimental results and showed 99.82% accuracy; then, nonlinear finite element analyses for 114 Models were conducted to be utilized as datasets to develop ANNs. To do so, the section height, flange width, web thickness, flange thickness, and steel yield strength were considered the input variables, while the ultimate buckling load was assumed to be the only output variable. ANN models are typically trained through algorithms such as LM, BR, and SCG; meanwhile, the novelty of this work is to take advantage of metaheuristics to optimize weights and biases of ANNs. In this regard, four metaheuristic algorithms, namely Particle Swarm Optimization, Colliding Body Optimization, and their developed version by applying Genetic Algorithm, Particle Swarm Optimization–Genetic Algorithm, and Colliding Body Optimization–Genetic Algorithm, have been used to train ANNs efficiently. Besides, their performances were evaluated using statistical relations like Coefficient of Correlation and Mean Square Error. The results illustrate that the trained models could accurately predict the ultimate buckling loads up to 99.8%, demonstrating the efficiency and precision of the present work.

Wing geometry and kinematic parameters optimization of bat-like robot fixed-altitude flight for minimum energy

In this paper, the geometry parameters (WGP) and kinematic parameters (WKP) of bat wings are optimized by using the revised quasi-steady aerodynamic model and genetic particle swarm optimization algorithm (GPSO) according to the structural characteristics and kinematic law of bat, to meet the requirement of minimum flapping energy and improve the endurance of bat-like robot. For the first time, the dynamic model of bat flapping motion is improved by considering the effects of active deformation, passive flexible deformation, gravity moment, and inertia moment of the wing structure. Based on the power density model, the optimization objective function is established, and the lift-weight ratio (LtoW), Reynolds number (Re), aspect ratio (AR), Strouhal number (St), and boundary constraints of each parameter are added to complete the optimization of WGP and WKP. The optimized aerodynamic forces and power densities are compared with the estimates for actual bats flying at a fixed-altitude, and the results showed that the optimized parameters resulted in more stable aerodynamic forces and reduced energy consumption by half. Finally, the sensitivity analysis of the influence of each parameter on the LtoW and power density is carried out to better understand the effect of each parameter on keeping fixed-altitude flight and reducing energy consumption. This study can provide the theoretical basis for reasonable parameter design of bat-like robot.

Efficient (2N+1) selective harmonic elimination in modular multilevel converters using an evolutionary many-tasking approach with prior knowledge

(2N+1) selective harmonic elimination pulse with modulation (SHE-PWM) is an important technique for a modular multi-level converter (MMC) to enhance the control ability and improve the fundamental wave amplitude accuracy of the output waveform. While finding optimal switch angles by solving the complex non-linear equation system is one of its main challenges. A current popular solution is to run a metaheuristic or evolutionary algorithm repeatedly on various modulation indexes. These methods ignore the fact that some of the optimization tasks are similar, and the optimization experiences could be shared among these similar tasks. To fill this gap, this study proposes an evolutionary many-tasking approach with prior knowledge (EMT-PK). EMT-PK facilitates positive knowledge transfer between similar tasks and is capable to solve all the sub SHE-PWM problems simultaneously through a single run. Numerical results on 7-, 9- and 11-level MMCs show that EMT-PK provides superior results for the (2N+1) SHE-PWM in comparison with 8 other widely used metaheuristics/evolutionary algorithms in this field including differential evolution (DE), ant colony optimization (ACO), teaching and learning-based optimization (TLBO), bee algorithm (BA), genetic algorithm (GA), particle swarm optimization (PSO), generalized pattern search (GPS) and asynchronous particle swarm optimization-genetic algorithm(APSO-GA). The superior performance of EMT-PK is further validated by the MATLAB Simulink simulation and the laboratory experiment on a 7-level MMC.

Improved GA-PSO algorithm for feature extraction of rolling bearing vibration signal

Abstract To better extract the characteristics of rolling bearing vibration signals, the author proposes a method based on improved genetic algorithm-particle swarm optimization (GA-PSO) algorithm. The common time-domain and frequency-domain feature index construction vectors were extracted based on vibration signals, for signal prediction, by establishing an improved particle swarm algorithm, and by optimizing the signal feature model of the support vector machine (SVM), the signal of the rolling bearing was predicted. The experimental results show that: After the author’s improved particle swarm algorithm optimizes SVM, the signal characteristic accuracy of the bearing is significantly higher, the regression fitting curve is smoother, although the fitting trend is basically the same, the error is significantly higher, this shows that it is feasible to optimize SVM’s rolling bearing signal characteristics based on particle swarm optimization, and proved the author’s improvement of the particle swarm algorithm, it is effective in optimizing SVM parameters. It is proved that the improved GA-PSO algorithm can better extract the characteristics of the vibration signal of the rolling bearing.

Operational stability control of a buried pipeline maintenance robot using an improved PSO-PID controller

Ensuring the structural safety of buried pipelines is critical for urban areas due to their capacity for transporting important liquid supplies and wastes. Due to the dangerous (i.e., harmful gas) and narrow environment of pipelines, using robots instead of manual work has become an excellent solution to maintain the pipelines. However, because of the poor perception (i.e., vision and global positioning system signal), the robots controlled manually are easily yawed or even overturned, resulting in an unstable and dangerous maintaining process. Therefore, our research aims to address the following research question: How can we evaluate and improve the operational stability of pipeline maintenance robots in the poor-perception pipeline environment? Aiming to address this question, this paper (i) proposes a framework of operational stability and its control strategies to evaluate and reduce the robot’s overturning probability, (ii) analyzes qualitatively the operational stability and its influential factors using zero-point moments and fuzzy theories, and (iii) implements a hybrid genetic algorithm-particle swarm optimization (GA-PSO) and proportional integral derivative (PID) controller to improve the operational stability of the buried pipeline robot. The proposed framework is simulated by the case of the Dadong Lake deep sewage tunnel system in Wuhan and its maintenance robot. Compared with the ZN-PID and BPSO-PID methods, this method can improve tracking accuracy by 88.9% and 78.6% during verification, respectively. Furthermore, laboratory-level experiments using a scaled-down robot are carried out to validate the effectiveness of the controller by using it in practice. The results of simulations and experiments show that the buried pipeline robots can be effectively controlled by the proposed framework.