Particle Swarm Optimization Genetic Algorithm Research Articles

Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography.

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively.

The multidepot vehicle routing problem with intelligent recycling prices and transportation resource sharing

The increasing focus on environmental regulations and the economic advantages of recycling has spurred interest in the design of multidepot reverse logistics networks (MDRLNs). In these networks, the growing use of intelligent recycling bins (IRBs) has been beneficial for both product recycling and standardizing recycling product pricing. Furthermore, collaboration and resource sharing enhance the efficiency of resource utilization and recycling. This study proposes a multidepot vehicle routing problem with time windows that incorporates intelligent recycling prices (IRPs) and transportation resource sharing (MDVRPTW-IRPTRS). Initially, a linear function is developed to define the relationship between the volume of returned products and IRPs. Subsequently, the problem is expressed as a mathematical model aiming to minimize total operating costs and maximize total recycling profits. Additionally, a hybrid algorithm that combines a three-dimensional (3D) k-means clustering algorithm with a self-adapting genetic algorithm-particle swarm optimization (SGA-PSO) is devised to determine the optimal solution for MDVRPTW-IRPTRS. The 3D k-means clustering algorithm is utilized to categorize IRBs within an MDRLN. The SGA-PSO algorithm incorporates elite preservation and self-adaptive update mechanisms to enhance the solution quality and algorithm convergence. A transportation resource sharing (TRS) strategy is integrated into the SGA-PSO, facilitating the allocation of shared vehicles to alternative recycling routes. A comparative analysis of SGA-PSO against other algorithms, including a hybrid genetic algorithm, an improved particle swarm optimization algorithm, and a hybrid genetic algorithm with variable neighborhood search, demonstrates its superiority in solving the MDVRPTW-IRPTRS. The model and algorithm are applied in a real-world case study in Chongqing, China, and the study discusses the optimized results under varying TRS strategies and IRP schemes, contributing to the development of an efficient and synergistic urban reverse logistics network. Moreover, the superior performance of the proposed approach is validated through the ablation experiments. This study offers valuable decision-making support for fostering an environmentally sustainable and resource-efficient city.

A New Mathematical Model for Cell Layout Problem considering Rotation of Unequal Dimensions of Cells and Machines

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time.

Research on the optimization of tourist attraction route planning through multiple big data

Tourism travel is a prevalent form of leisure and entertainment. This paper provides a brief overview of an attraction route planning algorithm based on multi-source data, which combines multi-source data to assess the effectiveness of the planned route and utilizes the particle swarm optimization (PSO) algorithm for path optimization. The genetic algorithm (GA) operations were incorporated with the PSO algorithm to enhance optimization performance. Subsequently, simulation experiments were conducted to compare the GA-PSO algorithm with using the PSO algorithm and GA. Moreover, a comparative analysis was performed on the performance of the path planning algorithm using single-source and multi-source data. The results demonstrated that the GA-PSO algorithm exhibited the fastest convergence in optimization search and achieved the best fitness value at stabilization. Among the three path schemes, the GA-PSO algorithm performed the best, followed by the GA, while the PSO algorithm was found to be the least optimal. Furthermore, path planning with multi-source data demonstrated better alignment with tourists’ landscape preferences, enabling personalized routes construction.

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance.

A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

An optimized approach for solar concentrating parabolic dish based on particle swarm optimization-genetic algorithm

Parabolic dish concentrators have demonstrated the highest thermal and optical efficiencies among the available concentrator options. This paper proposes a novel design approach for fabricating large parabolic dish concentrators by employing compliant petals optimized through Particle Swarm Optimization-Genetic Algorithm (PSO-GA). The design concept involves using cables to pull the outer corners of the petals towards the center, resulting in the creation of finely formed dish mirrors. These mirrors are constructed from thin, optimal-shaped metal petals with highly reflective surfaces. In addition, an analytical model is presented to optimize the bending stiffness of the petals by strategically arranging punched holes using PSO-GA. The proposed design concept is validated through the application of Finite Element Analysis and ray tracing software, specifically LightTools, as well as laboratory experiments. Based on the demonstration with a 1m-diameter parabolic dish, it was observed that a receiver surface with a radius of 3.5 cm could achieve an impressive sunlight collection efficiency of up to 98%. This innovative design approach offers several advantages, including simplified fabrication and transportation of flat mirror elements to field sites, which can potentially lead to cost reductions and highly efficient solar energy solutions.

Artificial intelligence-based optimal EVCS integration with stochastically sized and distributed PVs in an RDNS segmented in zones

The growing interest in electric vehicles (EVs) for transportation has led to increased production and government support through legislation since they offer environmental benefits such as reduced air pollution and carbon emissions compared to conventional combustion engine vehicles. This shift toward EV technology aligns with the goal of preserving the natural environment. To fully utilize EVs, effective management of the power grid is crucial, particularly in radial distribution network systems (RDNS) as they pose stress and deviation of power system parameters from their normal. This study proposes a novel strategy for maximizing EV utilization through EV charging stations (EVCSs) in an RDNS by considering factors such as load voltage deviation, line losses, and the presence of distributed solar photovoltaic systems at load centers. The research begins by segmenting the RDNS into zones, followed by the application of an artificial intelligence-based hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach known as hybrid GA–PSO. This approach identifies optimal locations for EVCSs integrated with photovoltaics within the network. Subsequently, the employment of individual GA and PSO algorithms to optimize EVCS placement focuses on minimizing power loss and enhancing voltage. The effectiveness of the hybrid GA–PSO algorithm is compared to that of separate GA and PSO methods. Extensive simulations using the IEEE 33-node test feeders validate the proposed techniques, demonstrating the usefulness of the hybrid GA–PSO algorithm in identifying optimal EVCS placement within each zone. The results also highlight the advantages and novelty of hybrid GA–PSO in achieving optimal EVCS placement with stochastically sized and distributed photovoltaic in an RDNS.

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

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.