Genetic Particle Swarm Optimization Research Articles

Electric Vehicle Charging Station Layout for Tourist Attractions Based on Improved Two-Population Genetic PSO

In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained.

Sensorless control of the BPMSM for artificial heart based on the improved SMO, the improved HFI and the GAPSO algorithm

BackgroundIncrease in volume in the bearingless permanent magnet slice motor (BPMSM) control system for artificial hearts is the major problem in the existing works. ObjectiveA sensorless control method of the BPMSM for the artificial heart is proposed in the study based on an improved sliding mode observer (SMO) and improved high-frequency injection method. Also, a full-speed rotor position detection method combining the high-frequency injection (FHI) method and sliding mode observer is designed. MethodologyIn this method, an improved high-frequency injection method was designed in the low-speed domain, and an improved sliding-mode observer was designed in the medium–high-speed domain. Besides, the speed domain switching method based on the genetic particle swarm optimization (GAPSO) algorithm was adopted at the critical point of the low-speed domain and the medium and high-speed domain to realize the smooth switching between different estimation methods, and then realize the sensorless control of the rotor in the full speed domain. Secondly, the simulation study by Simulink was used to compare the detection effects of the BPMSM rotor position and speed of the artificial heart pump under different methods are compared in this work. ResultThe results show that the speed estimation value and rotor position estimation value of the new method in this paper were closer to the actual value, the position estimation error is 3%, and the speed estimation error was within 1.5%. Further, the experimental study was carried out on the principal prototype of the sensorless control part of the artificial heart to verify the effectiveness of the proposed method. ConclusionThe method proposed in this paper has certain generality in the field of motor sensorless control technology, and has important reference value for researchers in this field.

Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm

In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.

Fault identification based on the KPCA-GPSO-SVM algorithm for seismic attributes in the Sihe Coal Mine, Qinshui Basin, China

Faults are geological structures that can cause disasters and thereby affect the safety of coal mines. To achieve improved fault interpretation accuracy during 3D seismic exploration of coal mines, we propose a seismic fault identification method based on a combination of kernel principal component analysis (KPCA), genetic particle swarm optimization (GPSO) and a support vector machine (SVM). The Dongwupan area of the Sihe Coal Mine in Shanxi Province, which mainly contains small faults, is the research area, and we extract 20 types of seismic attributes. According to the median difference between fault and nonfault data, we select the 12 leading seismic attributes with differences greater than 0.1 in descending order. Considering information redundancy and the nonlinear relationships among seismic attributes, we adopt the KPCA method to reduce and optimize the selected seismic attributes, thereby effectively capturing the main information contained in the data and eliminating noise. Moreover, we introduce the GPSO algorithm to effectively optimize the SVM model parameters, and we construct a KPCA-GPSO-SVM model to classify and predict faults. Through model testing, the average fault identification accuracy of the model is 98.89%. Relative to the KPCA-PSO-SVM, PCA-GPSO-SVM and GPSO-SVM models, the accuracy is improved by 3.3%, 7.8% and 16.7%, respectively. We apply the proposed model to predict the fault distribution in the research area, and we compare the predictions to the actual exposed faults. The results indicate that the KPCA-GPSO-SVM model can suitably realize fault distribution prediction in a mining area. Moreover, compared to manual fault interpretation, the proposed method is faster, more intuitive and can better identify small faults.

A Hybrid Genetic Particle Swarm Optimization Algorithm Based Fusion Protein Functionality Prediction

A fusion protein is a protein with at least two domains that are each encoded by a different gene and are combined into a single polypeptide by transcription and translation. For example, chromosomal rearrangement could result in the in vivo production of fusion proteins. One such fusion protein is the one responsible for chronic myelogenous leukaemia, the BCR-ABL protein. Recombinant DNA techniques could be used to create fusion proteins in vitro. By combining genes or portion of genes from similar or dissimilar organisms, fusion genes and proteins may be produced. But, real-time lab experiments for automated fusion protein functionality prediction are expensive and time-consuming. This paper proposes a novel Fusion Protein Functionality Prediction based on a Hybrid Genetic Particle Swarm Optimization (HybGPSO) algorithm to deal with this problem. The cellular component, biological process, and molecular function of an unannotated fusion protein by the GO consortium are the three functionalities predicted by this algorithm. The results of the experiments demonstrate that the proposed HybGPSO algorithm accurately predicts the function of fusion proteins.

Two-stage thermal-hydraulic optimization for Pillow Plate Heat Exchanger with recirculation zone parameterization

• PPHE is a promising alternative to conventional heat exchangers. • A two-stage thermal-hydraulic optimization method is proposed. • The developed PPHE model is evaluated with existing experimental data. • Average PEC of the two-stage optimized PPHE is improved by 29.3%. Pillow plate heat exchanger (PPHE) has become a promising alternative to conventional heat exchangers due to their satisfactory heat transfer performance and geometric flexibility. Nevertheless, the presence of recirculation zone (RZ) greatly restricts its thermal-hydraulic efficiency. In this study, a two-stage thermal-hydraulic optimization method with RZ parameterization was proposed. At the first stage, a genetic particle swarm optimization algorithm was developed, which combines a genetic algorithm with particle swarm optimization to maximize the thermal-hydraulic performance of PPHE. While at the second stage, the optimization objective was refined to further enhance the heat transfer characteristics based on geometrical parameters of RZ. The developed CFD model of PPHE was evaluated against existing experimental data and found to be in satisfactory agreement in terms of hydraulic diameter, Nusselt coefficient and pressure drop. Compared to the baseline design, the average PEC of the two-stage optimized PPHE was improved by 29.3% over a Reynolds number ranging from roughly 4,000 to 8,000, while the heat transfer area of RZ was reduced by 25.3% in average. The results revealed that a decreased welding spot pitch ratio contributes to the flow mixing between mainstream and the stagnant fluid downstream of the RZ, while smaller welding spot can significantly improve the turbulent energy dissipation of the mainstream, and therefore enhance the overall heat transfer.

Dynamic regional splitting planning of remote sensing satellite swarm using parallel genetic PSO algorithm

The strips from region splitting method are critical premise of meta-task generation and task planning for satellite swarm. To achieve the regional observation with high coverage and low overlap ratio and satellites usage, a novel region splitting algorithm is designed based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In the hybrid algorithm of GA and PSO, these two algorithms are executed paralleled, and then exchange the best genes and individuals periodically, to overcome the disadvantage of GA's poor local searching ability and PSO's low convergence speed. The encoding method and parallel iterative optimal method are given in detail. Finally, a simulation scenario is designed and executed to verify proposed algorithm, and the effects of algorithm parameters are analyzed by simulation results.

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

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

GAPSO-Optimized Fuzzy PID Controller for Electric-Driven Seeding.

To improve the seeding motor control performance of electric-driven seeding (EDS), a genetic particle swarm optimization (GAPSO)-optimized fuzzy PID control strategy for electric-driven seeding was designed. Since the parameters of the fuzzy controller were difficult to determine, two quantization factors were applied to the input of the fuzzy controller, and three scaling factors were introduced into the output of fuzzy controller. Genetic algorithm (GA) and particle swarm optimization (PSO) were combined into GAPSO by a genetic screening method. GAPSO was introduced to optimize the initial values of the two quantization factors, three scaling factors, and three characteristic functions before updating. The simulation results showed that the maximum overshoot of the GAPSO-based fuzzy PID controller system was 0.071%, settling time was 0.408 s, and steady-state error was 3.0693 × 10−5, which indicated the excellent control performance of the proposed strategy. Results of the field experiment showed that the EDS had better performance than the ground wheel chain sprocket seeding (GCSS). With a seeder operating speed of 6km/h, the average qualified index (Iq) was 95.83%, the average multiple index (Imult) was 1.11%, the average missing index (Imiss) was 3.23%, and the average precision index (Ip) was 14.64%. The research results provide a reference for the parameter tuning mode of the fuzzy PID controller for EDS.

Intelligent Control Strategy of Electrohydraulic Drive System for Raising Boring Power Head

The power head is the key part of the rock breaking work of the raise boring machine. Because the power head cannot adjust speed in time with the change in complex rock stratum, it leads to high failure rate, low work efficiency, and even accidents, so it is urgent to improve the controllability of the power head. In this paper, the electrohydraulic coupling mathematical model of the power head is established using the characteristic equations of dynamics and hydraulic components, and the control strategy of the fractional electrohydraulic drive system of the power head is proposed; genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) are used to adjust the parameters of FOPID, so as to improve the control effect of electrohydraulic system. The results show that the step response of WOA-FOPID control strategy is also better than that of genetic algorithm (GA) and particle swarm optimization (PSO). It can reach a stable state in 0.02 seconds, and the overshoot is only 0.12137%. The test verifies the correctness of the adaptive control and simulation results of the power head, which can effectively improve the adaptability of the power head to complex coal seams.

Optimized scheduling of resource-constraints in projects for smart construction

In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning.

Encryption method of annual bad debt accumulation information and reimbursement based on ant colony algorithm

In the enterprise financial information management, the information security of reimbursement and annual bad debt accumulation cannot be ignored. In order to solve the problem that the plaintext exceeds the byte limit in information encryption and affects the encryption calculation time, an annual bad debt accumulation information and reimbursement encryption method is proposed based on ant colony algorithm. We first define all the variables including encryption method, organizations’ information and reimbursement encryption, Ant colony algorithm and then we utilized elliptic curve digital signature to process the data and to prevent the annual bad debt accumulation information data from being tampered with or replaced in the transmission process. Furthermore, one dimensional Logistic chaotic map and Tent chaotic map with good randomness are selected to generate chaotic sequences as keys, and they are grouped based on ant colony algorithm. Each group is XOR with the previous group of ciphertext, which shortens the plaintext bytes. The design of the encryption method for the accumulated information of annual bad debt accrual is completed through the operation and the second round of encryption. The plaintext data files with file sizes of 10MB, 50MB, 100MB and 200MB are selected for serial and parallel encryption calculation. It shows that the average speedup ratio of this method is 3.90, which is 0.44 and 0.46 higher than the encryption methods based on genetic algorithm and particle swarm optimization algorithm, so it has better encryption performance.

Derivation of a Universally Valid Array Factor of a Conformal Arrays Based on Phase Compensation and Genetic Learning Particle Swarm Optimization

In this study, we investigated the recent deterioration of the radiation pattern performance of conformal arrays, which are applied to fields such as aircraft and vehicles. We analyzed the radiation pattern of conformal arrays in the array factor stage by combining previous studies on various beam-forming techniques for conformal arrays. To efficiently calculate and utilize the radiation pattern of conformal arrays, we derived an array factor based on phase composition for nonplanar arrays of three-dimensional (3D) coordinate systems. As an amplitude tapering method for controlling the sidelobe level of the derived 3D array factor, we propose a Bernstein polynomial generalization method based on Genetic Learning Particle Swarm Optimization. The proposed 3D array factor was verified using a cavity-backed patch antenna operating at the X-band through EM simulation of conformal arrays as a single element.

A novel relationship-oriented clustering approach for extracting relational patterns from the traffic tangled data

ABSTRACT Many models have been proposed in the literature of traffic engineering to investigate the relations between the prominent traffic variables such as velocity, density, and flow. However, in a specific period and environment, the traffic variables do not follow the same pattern discovered by a model and the observations might have various behavioral patterns. This means that a wide range of tangled data must be investigated and the aforementioned diverse patterns should be extracted so that the future values of the traffic variables can be predicted. This paper proposes a new approach based on which the relations between the traffic variables in the tangled data are detected. To show the practical value of this study, different procedures of the proposed approach are carried out for the traffic data of a highway in Iran. To detect the existing patterns of the collected data, an optimization regression-based model is developed based on which the traffic observations are segregated. The objective of this model is minimization of the Mean Squared Error () metric. The proposed approach fits multiple lines to the tangled data instead of a single one; therefore, the observations are segregated and diverse relations between the traffic variables will be revealed. The developed model is a mixed-integer non-linear mathematical formulation belonging to the class of NP-hard problems. Thus, two powerful meta-heuristics including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to solve the model and find the coefficients of the fitting lines. For a small number of observations, the outputs of the GA and PSO have been tested in terms of validity by comparing them to the optimal values provided by the LINGO software. The GA and PSO could reach the optimal values in 77% and 64% of their runs, respectively. The validity of the results have also been confirmed by conducting one sample t-tests. The coefficient of determination has been utilized to evaluate the goodness of the fitting lines acquired by the GA and PSO. The outputs show that the values of all fitting lines are above 0.80; therefore, it can be concluded that the GA and PSO have been successful in delivering high-quality fitting lines. Having acquired high-quality fitting lines, it was proven that the proposed approach has been sufficiently efficient and effective in detecting various behavioral patterns existing in the traffic tangled data.

Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

Image Model and Algorithm of Human Resource Optimal Configuration Based on FPGA and Microsystem Analysis

Human resources are the most dynamic factor among all productivity factors. The goal of human resource management is to achieve effective and optimal allocation of resources, while the goal of optimal allocation is to promote the realization of corporate goals. At present, the overall domestic human resource management is relatively backward, and most of it is nonstandardized management, and the level of informatization and technology is not high. Therefore, this paper proposes the image model and algorithm research of human resource optimization configuration based on FPGA and microsystem analysis. First, through the data analysis method, in-depth study of traditional resource allocation algorithms: genetic algorithm and particle swarm optimization algorithm, combined with machine learning algorithms, established a human resource optimization configuration model based on FPGA and microsystem analysis, and then optimized the configuration of human resources for the impact The standard deviation and variance measurement of 18 factors of the company, as well as the analysis of the matching degree between employees and positions, and required capabilities. Finally, it is concluded that the sample average is higher and the top three are employees’ positive spirit A12, employees’ executive ability A15, and the relationship between salary and performance A11. The sample averages are 4.615, 4.522, and 4.479, respectively; standard deviation of the three factors with the smallest values are good values A8, strong employee cooperation ability A17, and perfect reward and punishment system A16. This shows that the above considerations should be considered in the optimization of enterprise human resources. Moreover, the algorithm in this paper is easier to obtain the optimal solution, and it is easier to obtain the optimal allocation result of human resources.

NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing.

With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system. Then, a hybrid genetic particle swarm optimization method is adopted to allocate the channel resources effectively. Finally, simulation results verify that the proposed algorithm can significantly improve the spectrum efficiency of satellite communications.

Performance Comparison of Optimization Algorithm Tuned PID Controllers in Positive Output Re-Lift Luo Converter Operation for Electric Vehicle Applications

In this work, a Particle Swarm-Based optimization algorithm is proposed to optimize the PID controller used in the Positive Output Re-Lift Luo converter for the Electric Vehicle application. The novelty of the work lies in developing an improved PID controller to explore the reliability of the re-lift Luo converter. In addition, the optimization improves the effectiveness of the closed-loop system that possesses high voltage gain with esteemed power density and efficiency. With the Particle Swarm Optimization control algorithm, , , and values are tuned in such a way to obtain the appropriate steady-state condition with no peak overshoot or undershoot, much lesser settling time, no steady-state error, and improved dynamic performances. To analyze the system stability and to obtain better results, reduced-order state-space modeling of the Re-lift Luo converter is adopted, which offers high computational speed and accuracy. Initially, a PID controller is designed using the Zeigler Nicholas method, and the results are compared with the genetic algorithm and Particle Swarm Optimization algorithm. The time-domain analysis has been carried out to analyze and compare the converter’s performance. The simulation results were verified experimentally, and the results prove the robustness of the proposed converter with a PID controller optimized using the Particle Swarm Optimization algorithm.

Quality Evaluation for Physical Education Teaching in Colleges with Joint Neural Network

A new teaching curriculum for physical education in colleges has been proposed as part of the teaching reform. New developments in content, goals, and teaching methods have also occurred, and these have gradually become the focus of current physical education researchers. By developing a model for objectively, truly, comprehensively, and accurately evaluating college physical education teaching, it will be possible not only to improve the quality of physical education instruction and the effectiveness of faculty in achieving their goals but also to improve the quality of programs. This research work proposes a Joint Neural Network (JNN) composed of an Improved Support Vector Machine (ISVM) and improved Back Propagation (BP) network for evaluating physical education quality in colleges. When using a standard SVM to classify feature data, the parameters used have an impact on the SVM’s classification. This study introduces an improved SVM with hybrid optimization of PSO and GA algorithms, combining the properties of genetic algorithms and particle swarm optimization. As the existing BP network is trapped with a local optimum, this work proposes an optimized BP network algorithm using the bat algorithm. The improved SVM and improved BP are then combined to form a joint neural network for evaluating the quality of physical education in colleges. Comprehensive and systematic experiments validate the correctness and effectiveness of the algorithm proposed in this paper.

Performance Comparison of Particle Swarm Optimization and Genetic Algorithm Combined with A* Search for Solving Facility Layout Problem

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns <GA, A*> and <PSO, A*>. GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster.