Hybrid Genetic Algorithm Particle Swarm Optimization Research Articles

Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.

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.

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

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

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.

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.

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.

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.

Performance Analysis of an Optimized Asymmetric Multilevel Inverter on Grid Connected SPV System

The purpose of this research is to develop an efficient single-phase grid-connected PV system using a better performing asymmetrical multilevel inverter (AMI). Circuit component reduction, harmonic reduction, and grid integration are critical criteria for better inverter efficiency. The proposed inverter’s optimized topology requires seven unidirectional switches, three symmetric dc sources, and three diodes to produce an asymmetric fifteen level output; whereas, the same configuration will generate 7, 11, and 15-level output with an appropriate choice of dc source magnitudes. It is possible to reduce inverter losses and boost efficiency by decreasing the number of switches used. The integration of an asymmetric 15-level inverter with a grid-connected solar photovoltaic system is discussed in this article. A grid-connected solar photovoltaic (GCSPV) system is modelled and simulated using an asymmetric 15-level inverter. The dc sources of the 15-level inverter are replaced with PV sources. The results were analyzed with different operating temperatures and solar irradiance conditions. The GCSPV system is controlled by a closed-loop control system using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), and Hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) based Proportional plus Integral (PI) controllers. Grid voltage, grid current, grid power, and total harmonic distortion (THD) of grid currents were analyzed. The performance of the 15-level asymmetric inverter was evaluated by comparing the THD of the grid current and the efficiency of the grid-connected photovoltaic system.

A hybrid genetic-particle swarm optimization algorithm for multi-constraint optimization problems

A hybrid genetic-particle swarm optimization algorithm for multi-constraint optimization problems

Real-time path planning for autonomous vehicle based on teaching–learning-based optimization

This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.

A Hybrid PSO-GA Approach to Investigate Optimal Power Flow in a Hybrid Energy System based on Emission Level

Power system optimization, especially in the field of operation, has experienced essential changes in recent years regarding the introduction of the concept of the hybrid energy system (HES). As a result, the optimal power flow (OPF) problem, as the cornerstone of power system operation studies, has faced new challenges regarding the modeling of these complicated systems. In this article, the OPF in a HES is proposed using the hybrid particle swarm optimization-genetic algorithm (HPSO-GA) approach considering the fuel cost as the main objective and the emission cost as the primary objective function. As the mentioned objectives are counterintuitive, to obtain the optimal point, they should be considered simultaneously. To overcome this problem, the Pareto optimal front (POF) is applied and the multi-criteria technique for order performance by similarity to ideal solution (TOPSIS) is used simultaneously. The results of the simulation case study on an improved IEEE 14-bus standard system confirmed the performance of the proposed method against the mathematical method by applying MATLAB optimization.

Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO.

Study on prediction model of diesel engine with regulated two-stage turbocharging system based on hybrid genetic algorithm-particle swarm optimization method at different altitudes

ABSTRACT Aiming at the problem of complicated mechanism modeling for regulated two-stage turbocharging (RTST) system of diesel engine, a hybrid genetic algorithm–particle swarm optimization (GA-PSO) method was applied to optimize back propagation neural network (BPNN) performance prediction model of diesel engine. First, using a high-altitude (low-pressure) simulation test bench for diesel engines, a full-load performance test of RTST diesel engine at different altitudes was carried out, and the regulation rules of diesel engine intake characteristics and two-stage turbocharger (TST) matching characteristics with altitude were obtained. Based on the test data, the BPNN is used to establish the boost pressure prediction model of the RTST system of the diesel engine. For the problem of slow convergence and low prediction accuracy of the BPNN, adaptive improvement measures such as momentum and learning rate are introduced, and the GA-PSO optimizes the weights and thresholds of BPNN. The performance comparison shows that hybrid GA-PSO is superior to particle swarm optimization (PSO) and genetic algorithm (GA) in terms of evolution speed and convergence accuracy. Genetic algorithm–particle swarm optimization–back propagation neural network (GA-PSO-BPNN) model has higher prediction accuracy and higher accuracy than particle swarm optimization–back propagation neural network (PSO-BPNN) model and genetic algorithm–back propagation neural network (GA-BPNN) model. Compared with the original model, the root-mean-square error of the model is reduced by 50%, the mean error percentage (MEP) is reduced by 38%, and the sum of the squared errors is reduced by 79%. The performance verification of the GA-PSO-BPNN model shows that the MEP of the model prediction results is 5.5%, and the GA-PSO-BPNN model has higher prediction accuracy.

Multi-AGV scheduling for conflict-free path planning in automated container terminals

Path planning and integrated scheduling are two important problems to be resolved in the design of any automated container terminal. There has been relatively little research, however, on automated guided vehicles (AGVs) conflict-free path planning with quay cranes (QCs) and rail-mounted gantry (RMG) cranes. This paper combines the two problems to realize the integrated scheduling of multi-AGV with conflict-free path planning. A mixed integer programming model based on path optimization, integrated scheduling, and conflicts and deadlocks is established to minimize AGVs delay time under the condition that the task allocation is known. A series of small-scale and large-scale experiments are conducted to validate the availability of Hybrid Genetic Algorithm-Particle Swarm Optimization (HGA-PSO) with fuzzy logic controller to adaptive auto tuning. Dynamic simulation of the path nodes indicates that the proposed model indeed can resolve the AGV conflict and deadlock problem and may be practically applicable to existing automated container terminals.

Novel Naphtha Molecular Reconstruction Process Using a Self-Adaptive Cloud Model and Hybrid Genetic Algorithm–Particle Swarm Optimization Algorithm

Naphtha is an important product of crude oil and has widespread industrial applications. Additionally, the composition of naphtha is influenced by the procurement and operations of crude oil, which...

Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle

Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.

A novel hybrid GA–PSO framework for mining quantitative association rules

Discovering association rules is a useful and common technique for data mining in which dependencies among datasets are shown. Discovering the rules from continuous numeric datasets is one of the common challenges in data mining. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the criteria of support and confidence. By drawing on two heuristic optimization techniques, to wit, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, a hybrid algorithm for extracting quantitative association rules was developed in this research. Accurate and interpretable rules result from the integration of the multiple objectives GA with the multiple objective PSO algorithms, which redresses the balance in the exploitation and exploration tasks. The useful and appropriate rules and the most suitable numerical intervals are discovered by proposing a multi-criteria method in which there is no need to discretize numerical values and to determine threshold values of minimum support and confidence. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness and comprehensibility. The results gained over five real-world datasets evidence the effectiveness of the proposed method. By hybridization of the GA and the PSO algorithm, the proposed approach has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage.

Hybrid particle swarm optimization-genetic algorithm trained multi-layer perceptron for classification of human glioma from molecular brain neoplasia data

Multi-Layer Perceptron (MLP) is among the most widely applied Artificial Neural Networks (ANNs). Multi-Layer Perceptron (MLP) requires specific designing and training depending upon specific applications. This paper deals with the high-dimensional problem of classification of human glioma from Molecular Human Brain Neoplasia Data by designing a Multi-Layer Perceptron (MLP) which is trained through hybridizing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results are compared with individual algorithms in terms of convergence rate, Mean Squared Error (MSE) and classification accuracy.

Design and energy management optimization for hybrid renewable energy system- case study: Laayoune region

The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Abstract Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine (SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization (GA-PSO) method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plug-in, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.