Parameter Identification of a Novel Controllable Excitation Feeding Platform Based on Improved Particle Swarm Optimization

In the area of cloud computing load balancing, the Particle Swarm Optimization (PSO) algorithm is neoteric and now praised highly, but recently a more neoteric algorithm which deploys the classifier into load balancing is presented. Besides, an algorithm called red-black tree which is aiming at improving the efficiency of resource dispatching is also praised. But the 3 algorithms all have different disadvantages which cannot be ignored. For example, the dispatch efficiency of PSO algorithm is not satisfying; although classifier and red-black tree algorithm improve the efficiency of dispatching tasks, the performance in load balancing is not that good, as a result the improved PSO algorithm is presented. Some researches are designed to get the advantages of new algorithm. First of all, the time complexity and performance for each algorithm in theory are computed; and then actual data which are generated in experiments are given to demonstrate the performance. And from the experiment result, it can be found that for the speed of algorithm itself PSO is the lowest, and the improved PSO solve this problem in some degree; improved PSO algorithm has the best performance in task solving and PSO is the second one, the red-black and Naive Bayes algorithm are much slower; PSO and improved PSO algorithm perform well in load balancing, while the other two algorithms do not do well.

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

Ultra-high performance concrete has excellent mechanical properties such as ultra-high strength and high durability, and has a broad application prospect in the field of bridge engineering. To make full use of the superior mechanical properties of UHPC and reduce the cost of the bridge, the particle swarm optimization algorithm is used to optimize the structural design of the UHPC beam bridge; Aiming at the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, the nonlinear adaptive weight update method is used to improve the particle swarm optimization algorithm. Based on the above-mentioned improved particle swarm optimization algorithm, the optimal design method of ordinary reinforced UHPC beams and prestressed UHPC beams with commonly used highway spans is studied. The reliability of the optimized structural limit state is analyzed by Monte Carlo (MC) importance sampling method. The results show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm has higher convergence speed and convergence accuracy. The optimal height-span ratio of ordinary reinforced UHPC beams decreases with the increase of the span, and the construction cost gradually increases; The optimal height-span ratio of the prestressed beam decreases first and then increases with the increase of the span, and the construction cost gradually increases. The calculation results of load capacity reliability indexes of optimization results are all higher than the target reliability indexes of similar components stipulated in China’s “Uniform Standard for Structural Reliability Design of Highway Engineering”.

It is of great significance to study how to use intelligent algorithm to optimize the scheduling of microgrid, so as to improve the operation efficiency of microgrid. In this paper, particle swarm optimization(PSO) algorithm is improved by nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors. Compared with PSO, the global search ability of the improved particle swarm optimization(IPSO) algorithm is improved. IPSO is used to optimize the scheduling of the microgrid grid connection model. The example results show that nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors can obviously improve PSO. IPSO has faster convergence and lower comprehensive cost of microgrid scheduling. This paper provides an effective method for the day ahead economic scheduling of microgrid.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to particle swarm optimization (PSO) algorithm, which increases the search capability of PSO algorithm considerably. The proposed algorithm initializes a new search with harmony pitch adjusting or random selection when PSO search direction is estimated incorrectly. This can provide further opportunities of finding better solutions for the particle swarm by guiding the entire particle swarm to promising new regions of the search space and accelerating the search. PSO, HPSO and IHPSO, as well as other advanced PSO procedures from the literature were compared on several benchmark test functions extensively. Statistical analyses of the experimental results indicate that the performance of IHPSO is better than the performance of PSO and HPSO.

Although there are many methods or models widely applied to evaluate water resources carrying capacity, the evaluation result is qualitative not quantitative. In order to quantificationally evaluate groundwater carrying capacity in irrigation district, multi-objective evaluation model of groundwater carrying capacity was established, which includes four objectives, i.e., GDP, grain yield, population and COD emissions. The multi-objectives model can factually describe groundwater carrying capacity for social economy subsystem and ecological environment subsystem in irrigation district. The improved Particle Swarm Optimization (PSO) algorithm was built and adopted to solving the groundwater carrying capacity multi-objective model. In the improve PSO algorithm, linear inertia weight and chaos variation operator are employed to improve basic algorithm global searching capacity. The model was applied to quantificationally evaluate groundwater carrying capacity of People's Victory Canal irrigation district in current year and in 2010 level year.

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

The particle swarm optimization (PSO) algorithm is an intelligent optimization algorithm and has been successfully applied to many fields. However, it has the deficiencies of the poor search capability and the low calculation efficiency. To solve the problems, the genetic algorithm is introduced to improve the PSO algorithm in the paper. Then, three simulated examples were used to assess the ability of the improved PSO algorithm. The results showed that the improved PSO algorithm could jump out of the local minimum and achieve the better solution. Furthermore, the paper reveals that the improved PSO algorithm seems to be an improvement on the original PSO algorithm.

Standard Particle Swarm Optimization(PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization.In order to overcome the shortcomings,an improved PSO algorithm was proposed.The proposed algorithm integrated the attraction-repulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula,and thus maintained population diversity and enhanced the ability of particle to escape from the local optima.The experimental results demonstrate that the proposed algorithm outperforms two existing variants of the PSO algorithm in terms of convergence accuracy while improving the velocity of convergence in the later evolution phase and avoiding premature convergence problem effectively.

Due to the characteristics of nonlinearity and strong coupling, the control system for unmanned helicopters is challenging to design. Coordinated turn is a basic mode for unmanned helicopters to complete complex actions autonomously. To achieve higher control performance, this paper proposes a fuzzy PID coordinated turn control method based on improved particle swarm optimization (PSO) algorithm. Firstly, the mathematical model of an unmanned helicopter is established, and the control strategy of coordinated turn mode is analysed. Then, the attitude and altitude controllers based on fuzzy PID for coordinated turn are designed. In order to compensate for the shortcoming that quantization factors and proportional factors of fuzzy PID controllers depend too much on experience, the differential evolution (DE) method, local convergence judgement, and linearly decreasing inertia weight are presented as improvements to the PSO algorithm to optimize factors. Finally, the results of the simulation demonstrate that the position, speed and attitude of the unmanned helicopter can react quickly and the corresponding output value is accurate and stable in coordinated turn mode. So the coordinated turn control method for unmanned helicopters is proven to be feasible and effective.

To address the significant scheduling challenges arising from high-penetration renewable integration and coupled multi-energy loads, this study examines the operational scheduling of an integrated energy system (IES) that incorporates system operators, user aggregators, electric vehicles, and other stakeholders. First, the flexibility demand and supply resources in the IES were analyzed, and flexibility indicators were quantified. Subsequently, a multi-objective bi-level optimization model considering flexibility and multi-entity participation was established for the IES’s low-carbon economic dispatch. The upper-level model considered the IES operator’s revenue and system flexibility, incorporating a green certificate carbon trading mechanism, while the lower-level model accounted for user aggregator costs and electric vehicle self-benefits, with interactions between the two levels through energy prices and purchase quantities. Finally, an improved Particle Swarm Optimization (PSO) algorithm was employed to solve the proposed upper-level model, and CPLEX 12.10 software was used for the lower-level model. A typical scenario in northern China was selected to validate the proposed model. The results demonstrated that the proposed model balanced system economy and flexibility compared to the traditional single-objective economic dispatch. Compared with only considering the benefits of operators, the proposed model can balance the interests of multiple parties. Additionally, compared to the traditional PSO algorithm, the improved PSO algorithm reduced the number of iterations at convergence by 52.0%, improved the closeness of the obtained optimal solution to the ideal solution by 7.5%, and had better convergence and optimization performance.

To improve the accuracy of LVRT control parameter identification for DFIG, a method is proposed based on an improved particle swarm optimization (PSO) algorithm. Firstly, a single DFIG connected to an infinite bus system model is established using the MATLAB/Simulink simulation platform. Subsequently, logistic chaos mapping is employed to initialize the population based on the standard PSO algorithm. An adaptive parameter adjustment mechanism is introduced, along with the Lévy flight strategy integrated into the algorithm’s flow. Finally, based on the residual measured operating conditions, an analysis and verification of the identified parameter accuracy are conducted. Simulations demonstrate the effectiveness and accuracy of the proposed parameter identification method. The results indicate that the proposed approach can rapidly and accurately identify the LVRT control parameters of DFIG. This method utilizes advanced signal processing techniques and machine learning algorithms to analyze and model real-time data from the DFIG system, enabling the rapid and accurate identification of the DFIG low-voltage ride-through saturation control parameters. This provides researchers and engineers with an effective tool to better understand and optimize the performance of DFIG systems, thereby driving greater progress in research and practice in this field.

In this paper, we improved the particle swarm optimization (PSO) algorithm with inertial weight by adopting the sigmoid function. The IPSO is applied to structural damage identification in frame structures. Firstly the theoretical background of the improved PSO (IPSO) algorithm is presented. Secondly, we use a suite of four famous benchmark functions to evaluate the algorithm performance. Some comparative studies on single and multi-damage of a 2-story rigid frame structure have been carried out. The results of the numerical simulation demonstrate that the approach can detect the structural damage with higher accuracy.

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.