Particle Swarm Optimization Genetic Algorithm Research Articles

Research on Optimal Torque Control of Turning Energy Consumption for EVs with Motorized Wheels

This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality.

A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting

Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA–PSO–CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA–PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the proposed GA–PSO–CNN model decreased by 1.13–9.55%, 0.46–7.98%, and 3.28–19.29%, respectively, in different seasons, compared with Single–CNN, PSO–CNN, ISSO–CNN, and CHACNN models. The convolution kernel size and number in each convolution layer were reduced by 5–18.4% in the GA–PSO–CNN model.

Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

Deriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.

Slipping Detection of Electric Locomotive Based on Empirical Wavelet Transform, Fuzzy Entropy Algorithm and Support Vector Machine

During the running of electric locomotives, a large amount of operating data will be stored through the Analog-Digital Converter (ADC) and the recording oscillograph of the on-board Traction Control Unit (TCU). Analysis based on mass data to detect slipping is of great significance for saving energy, improving the performance and safety of the traction control system of electric locomotives. Therefore, after analyzing and summarizing the advantages of Empirical Wavelet Transform (EWT) and Fuzzy Entropy (FE) algorithm in data processing and the classification theory of Support Vector Machine (SVM), this paper proposed a SVM slipping detection method based on EWT and FE algorithm. Firstly, feature extraction is carried out on the locomotive wheelset velocity data collected by the on-board velocity sensor, that is, the feature vector of data is extracted by calculating fuzzy entropy value after EWT, and the velocity data is converted into feature matrix for slipping-detection of SVM. Then considering the defects of traditional optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the parameter optimization of SVM, Genetic Particle Swarm Optimization Algorithm (GAPSO) is proposed to optimize the parameters of SVM classification model, and the slipping detection model of SVM is established through training samples, which realizes high-precision slipping detection. Finally, combined with a large number of locomotive data, the comparative analysis test is carried out, the results show that the feature extraction method based on EWT and FE algorithm has excellent ability of mode decomposition and feature extraction, GAPSO has efficient optimization ability, and the proposed slipping detection method can accurately detect slipping.

Shift quality optimization control of power shift transmission based on particle swarm optimization–genetic algorithm

A clutch control strategy is proposed to improve the shift quality of power shift transmission and solve the power cycle phenomenon in the shifting process. The strategy effectively avoids the power cycle phenomenon by analyzing the change in the transmission torque and the relationship between the rotating speed of the driving and driven disks of a shifting clutch. The evaluation indexes of the shift quality of power shift transmission are sliding friction power loss, impact degree, and torque oscillation amplitude. Particle swarm optimization (PSO), combined with a genetic algorithm, is used to optimize the shift quality of power shift transmission, solving the problem in which basic PSO frequently converges to the local optimal solution. Simulations and experiments are performed on the optimization results. The simulation and test results show that the proposed control strategy can ensure that the rotating speed of the driving disk is always higher than that of the driven disk when the clutch is insliding and wearing states. Thus, this control strategy effectively avoids power cycle when shifting and improves shift quality.

The new evidence reasoning based pharmaceutical inventory models with stochastic deterioration rates and lead times using PSO and GA

Pharmaceutical inventory management is critical because insufficient pharmaceutical items and their misusage affect the lives of humans and cause financial losses. Conversely, deteriorating items and lead-time have special importance in pharmaceutical inventory issues, especially as uncertainty appears in both. Different procedures are considered to deal with uncertainty, including fuzzy systems as well as probabilistic techniques. The present study develops a novel technique according to the evidential theory approach, aimed at solving an inventory model with stochastic deterioration rates and lead-times appearing in a single-vendor (pharmaceutical company) multi-buyer (hospitals) inventory system. It seems that the evidence approach is effective in dealing with interval, imperfect, inaccurate, and missing (ignorance) data. The lead time demand of the hospitals is log-normal, and the shortage of hospitals is thoroughly back-ordered. Particle swarm optimization (PSO) and genetic algorithm (GA) solve this problem. Ultimately, the results are illustrated using the numerical example and the impacts of the main parameters. Highlights Develop the new pharmaceutical supply chain models based on the evidential theory approach Design single-vendor (pharmaceutical company) multi-buyer (hospitals) system with stochastic deterioration rates and lead times Considering interval, incomplete, imprecise and missing (ignorance) data in healthcare systems for make decision in the inventory problem Using genetic algorithm (GA) and particle swarm optimization algorithm (PSO)

Optimal allocation of wind-solar storage capacity of microgrid considering carbon emission reduction benefits

In the context of vigorously advocating the transformation of electric energy production to green and low emission, it is very important to rationally allocate the wind-solar storage capacity of micro-grid. Based on this, this paper aims at the micro grid with wind-solar storage. Firstly, the output model of wind-solar storage unit is established, combined with the system scheduling strategy. Then, the optimization objective was to minimize the total cost of investment and operation, and the benefits of carbon emission reduction were taken into account. Genetic particle swarm optimization algorithm is used to solve the optimal capacity allocation. Finally, according to the calculation results of the example, the proposed wind-solar storage capacity configuration considering the benefits of carbon emission reduction can effectively reduce the cost and carbon emissions of the microgrid.

On Comparison of Two Approaches for Steam Turbine Optimizing Start-Up

According to the operating conditions, operating rules and procedures of the steam turbine, a 300MW steam turbine cold start mathematical model was established, and the finite element 3-D modeling and simulation of the 300MW condensing steam turbine rotor was carried out. By using ANSYS. Calculate the stress value of cold start-up according to the cold start curve under standard conditions. The start-up curve design of the initial working condition is inefficient. This paper uses genetic algorithm and particle swarm optimization algorithm to optimize and analyze the turbine mathematical model, draw a new cold start-up curve, and then load the cold start curve into the model. The ANSYS calculates the optimal thermal stress value. After calculating the stress, compare the results on the initial working condition with the results after two optimizations. In contrast, under the premise of ensuring that the thermal stress value of the turbine rotor does not exceed the limit value of the rotor, the startup time is shortened and the economy is improved.

Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line

ABSTRACT Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of consumers. In some assembly lines, there is a considerable variation in cycle times, which reduces production efficiency. However, a bypass sub-line that undertakes a portion of product assembly operations reduces the variation in the assembly times. In the present paper, the following three objective functions are considered simultaneously: (1) minimizing the variation in the actual and required production capacity, (2) minimizing total utility work, and (3) minimizing total variation in production rate. The formulated sequencing problem is solved by two different Multi-Objective Evolutionary Algorithm (MOEAs) including the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). Then, some numerical examples are conducted, and the efficiency of the two proposed algorithms is measured based on some comparison metric.

Research on Pipeline Defect Classification Based on SVM

The defect identification and evaluation of buried steel pipeline is a long-term challenge in the field of pipeline detection, and the prerequisite for efficient identification of defects is the accurate extraction of pipeline damage signals. Aiming at the characteristics of buried pipeline defect signals, a method of pipeline defect signal extraction and recognition is proposed, which is based on support vector machine (SVM). A dictionary is obtained by learning from the original signal, the dictionary is used to construct a sparse model of the defect signal using a regularized orthogonal matching pursuit algorithm, and the feature vector of the signal is obtained according to the compressed sensing theory. Furthermore, multi-classification SVM is used to establish a mapping relationship between the feature vector of the defect signal and the actual defect type of the pipeline, and Genetic Algorithm-Particle Swarm Optimization is used to guide the selection of SVM parameters. The results showed that the proposed classification method can realize the accurate division of the damage degree of pipeline defects.

Study on Active Disturbance Rejection Control of a Bearingless Induction Motor Based on an Improved Particle Swarm Optimization–Genetic Algorithm

To overcome the limitations that active disturbance rejection control (ADRC) system of a bearingless induction motor (BIM) has difficulty in tuning parameters depending on experience to select parameters, an ADRC strategy based on improved particle swarm optimization-genetic algorithm (IPSO-GA) is proposed. Based on the orientation of the air-gap magnetic field, the first-order and the second-order ADRC are, respectively, designed for the BIM rotation and suspension parts according to the different order of the BIM system. Then, the parameters of the basic particle swarm optimization algorithm are optimized by considering the characteristics of the basic particle swarm algorithm parameters, and the crossover and mutation operations are introduced to enhance global search capability. Meanwhile, through the performance test based on the test function, the performance of IPSO-GA is verified, and the parameters of ADRC are adjusted by IPSO-GA. In addition, this strategy is analyzed with simulation in MATLAB/Simulink and verified on an experimental prototype. Both simulation and experimental results show that the proposed strategy not only effectively improves the starting performance and antidisturbance ability of the BIM but also reduces the maximum radial offset of the rotor and improves the suspension precision of the motor.

Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid

Due to rapid population growth, technology, and economic development, electricity demand is rising, causing a gap between energy production and demand. With the emergence of the smart grid, residents can schedule their energy usage in response to the Demand Response (DR) program offered by a utility company to cope with the gap between demand and supply. This work first proposes a novel optimization-based energy management framework that adapts consumer power usage patterns using real-time pricing signals and generation from utility and photovoltaic-battery systems to minimize electricity cost, to reduce carbon emission, and to mitigate peak power consumption subjected to alleviating rebound peak generation. Secondly, a Hybrid Genetic Ant Colony Optimization (HGACO) algorithm is proposed to solve the complete scheduling model for three scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. Thirdly, rebound peak generation is restricted by using Multiple Knapsack Problem (MKP) in the proposed algorithm. The presented model reduces the cost of using electricity, alleviates the peak load and peak-valley, mitigates carbon emission, and avoids rebound peaks without posing high discomfort to the consumers. To evaluate the applicability of the proposed framework comparatively with existing frameworks, simulations are conducted. The results show that the proposed HGACO algorithm reduced electricity cost, carbon emission, and peak load by 49.51%, 48.01%, and 25.72% in scenario I; by 55.85%, 54.22%, and 21.69% in scenario II, and by 59.06%, 57.42%, and 17.40% in scenario III, respectively, compared to without scheduling. Thus, the proposed HGACO algorithm-based energy management framework outperforms existing frameworks based on Ant Colony Optimization (ACO) algorithm, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), Hybrid Genetic Particle swarm Optimization (HGPO) algorithm.

Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR. A hybrid genetic particle swarm optimization algorithm is introduced, and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply. The fitness function of hybrid genetic particle swarm optimization is a multi-objective function, which combined the current and voltage, so that the dynamic performance of the closed-loop system can be better. The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R. The simulation results show that adoption of this method leads to good transient responses, and the computational time is shorter than in the traditional trial and error methods. The results presented in this paper show that the proposed method is robust, efficient and feasible, and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

Asynchronous Particle Swarm Optimization-Genetic Algorithm (APSO-GA) Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.

Network Lifetime Improved Optimal Routing in Wireless Sensor Network Environment

In the field of Wireless Sensor Networks, major problem arises due to the unbalanced consumption of energy. The unbalanced consumption will reduce the lifetime of a network performance. In the existing work, replacement of node is a difficult process and conservation of network parameters is not concentrated. It leads to the failure of network in different situations. These issues are resolved by introducing a density aware optimal clustering which is used to perform clustering to ensure the data transmission in a reliable manner. In the proposed work, Energy Balancing and Optimal Routing Based Secured Data Transmission based on clustering is introduced. It enhances the lifetime uniformly deployed data-gathering sensor networks using a balanced energy consumption technique. Ant Colony Optimization method is used to select the optimum head of the cluster to enhance lifetime of a network. Different techniques have been proposed for optimal edge disjoint routing. The data packets are forwarded in an optimum way by these techniques. Hybrid Genetic Particle Swarm Optimization algorithm is used to select the optimum edge disjoint route paths. The parameters of Quality of Service like reliability, capacity of processing, bandwidth and energy are considered to make an optimum selection. Based on the similarity values, sensor data’s are grouped and these data are aggregated to ensure the consumption energy which makes an optimum data transmission.

A smart predictor used for lithologies of tight sandstone reservoirs: a case study of member of Chang 4 + 5, Jiyuan Oilfield, Ordos Basin

The common scientific issue presented by lithology prediction of tight sandstone reservoirs is that most primary lithologies cannot be distinguished effectively within the classic crossplots since they universally have similar logging responses. Lithology prediction actually is an issue of pattern recognition, and has been proved its best solver currently is machine learning technique. XGBoost is demonstrated to be a high-efficient predictor, while two factors, setting of hyper-parameters and dimensionality reduction of learning dataset, severely limit its computational performance. Genetic algorithm-particle swarm optimization (GA-PSO) and continuous restricted Boltzmann machine (CRBM) are introduced to optimize hyper-parameters and reduce amount of learning variables for the calculation of XGBoost, respectively, then a smart predictor CRBM-GA-PSO-CRBM proposed. Data used for validation is collected from the wells in the member of Chang 4 + 5, Jiyuan Oilfield, Ordos Basin. Three experiments are designed to verify prediction capability of the proposed model. Compared to other validated models, the higher prediction accuracies are all generated by the proposed model in three experiments, well expounding the proposed model is capable to produce reliable predicted lithologies, and has a better generalization and application prospect in the study field of lithology prediction.

Economic optimization of heat exchanger networks based on geometric parameters using hybrid genetic-particle swarm algorithm technique

PurposeThe purpose of this study is to provide a method for designing the shell and tube heat exchangers and examine the total annual cost of heat exchanger networks from the economic view based on the careful design of equipment.Design/methodology/approachAccurate evaluation of heat exchanger networks performance depends on detailed models of heat exchangers design. The simulations variables include nine design variables such as flow direction determination of each of the two fluids, number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter and tube pitch. The optimal designing of the heat exchangers is based on geometrical and hydraulic modeling and using a hybrid genetic particle swarm optimization algorithm (PSO-GA) technique. In this paper, optimization and minimization of the total annual cost of heat exchanger networks are considered as the objective function.FindingsIn this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement. PSO-GA algorithms have been used to minimize the total annual cost, which includes investment costs of heat exchangers and pumps, operating costs (pumping) and energy costs for utilities. Three case studies of four, six and nine streams are selected to demonstrate the accuracy of the method. Reductions of 0.55%, 23.5% and 14.78% are obtained in total annual cost for the selected streams, respectively.Originality/valueIn the present study, a reliable method is used to simulate and optimize design parameters and the economic optimization of the heat exchanger networks. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the PSO-GA methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to case studies demonstrates its ability to accurately design heat exchangers to optimize the objective function of the heat exchanger networks by giving the detail of design.