Improved Particle Swarm Algorithm Research Articles

Research on an Improved Particle Swarm Algorithm in DV-HOP Algorithm

The node positioning error in wireless sensor has always been the focus of research. A way of an improved particle swarm optimization algorithm being proposed to correct the DV-Hop error positioning method, which can reduce the estimate error for the distance between unknown nodes and anchor nodes. The matlab simulation results show that the improved algorithm can effectively improve the positioning accuracy of the sensor nodes than the traditional algorithm. The simulation results have verified the effectiveness of the improved algorithm.

Regional Traffic Timing Plan Optimization Based on Improved Particle Swarm Algorithm

The modern traffic control timing plan aims at raising transportation efficiency of vehicles by making full use of existing road resources. So we built a region traffic control model; its performance of model is measured by minimum average travel time of vehicles, its average delay model for single crossroad is derived from the revised HCM2000; and then it is transformed into objective function of optimization. To enhance the global and local searching ability, the improved exponential inertia weight factor is taken for the particle swarm algorithm. So basing MATLAB simulation tool, we calculated timing plan on under-saturation traffic flow condition and saturation traffic flow condition by the classic particles swarm optimization algorithm and improved particle swarm algorithm respectively. The simulation results show that the improved particle swarm algorithm can effectively avoid that the particles fall into local optimal solution; at the same time, the improved algorithm can reasonably distribute green signal time for every phase, the timing plan can effectively shorten average vehicle delay time and improve the transportation efficiency of road.

Application of an Improved PSO Algorithm to Control Parameters Auto-tuning for Bi-axial Servo Feed System

To improve dynamic performance of servo feed system and aim at automatically calculating optimal controller parameters to increase contour accuracy and manufacturing efficiency. An improved particle swarm algorithm (IPSO) is adapted to optimize control parameters on line, and to match the control parameters of bi-axial servo feed system in this paper. Linearly decreasing acceleration weights is introduced to improve global searching ability in early stage and converge to global optimization at the end of the search. At the same time, adaptive maximum speed limit is introduced to control the scale of search and improve convergence efficiency. The experimental results demonstrate that the novel method is effective at reducing the follow error and contour error.

Vehicle Routing Problem Based on Particle Swarm Optimization Algorithm with Gauss Mutation

In order to solve the vehicle routing problem, this paper introduces the Gauss mutation, which is based on the common particle swarm algorithm, to constitute an improved particle swarm algorithm (NPSO). In the process of solving vehicle routing problem, the NPSO is encoded by integer and proposes a new way to adjust the infeasible solutions. The particles are divided into two overlapping subgroups, and join the two-two exchange neighborhood search to iterate. Finally, the simulation experiments show that the proposed algorithm can get the optimal solution faster and better, and it has a certain validity and practicability.

Food Emergency Logistics Mode Based on Improved Particle Swarm Algorithm

Food Emergency Logistics Mode Based on Improved Particle Swarm Algorithm

SA-PSO based optimizing reader deployment in large-scale RFID Systems

The RFID technologies are expected to revolutionize various industrial areas and the application of large-scale RFID systems has been a trend. Satisfying a set of imperative constrains and optimizing a set of objectives, the reader deployment is the primary problem for the most challenging RFID network planning (RNP), and needs to be reasonably solved to operate the large-scale RFID systems in an optimal fashion. By taking the coverage, signal interference and load balance as the optimization objectives and deducing the objective ranges, the reader deployment is conducted as a problem of multi-objectives combination optimization. And then, by introducing the Metropolis rule of Simulated Annealing Algorithm, an improved Particle Swarm Algorithm is proposed (SA-PSO) to solve this problem, which can restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm. The simulation results show that the addressed SA-PSO algorithm is superior to the compared algorithms in coverage optimization, and the whole system performance can be enhanced by optimizing the signal interference and load balance among the deployed readers.

A Novel Reactive Power Compensation Approach Based on Particle Swarm Optimization

The particle swarm optimization (PSO) algorithm as a stochastic search algorithm for solving reactive power optimization problem. The PSO algorithm converges too fast, easy access to local convergence, leading to convergence accuracy is not high, to study the particle swarm algorithm improvements. The establishment of a comprehensive consideration of the practical constraints and reactive power regulation means no power optimization mathematical model, a method using improved particle swarm algorithm for reactive power optimization problem, the algorithm weighting coefficients and inactive particles are two aspects to improve. Meanwhile segmented approach to particle swarm algorithm improved effectively address the shortcomings evolution into local optimum and search accuracy is poor, in order to determine the optimal reactive power optimization program.

Two regional power system PSO PID Control Research

In this paper, we propose a PID parameter tuning of particle swarm optimization for multi-objective optimization characteristics of two regional power system PID controller design. By defining a comprehensive consideration of system output overshoot, rise time and the fitness function term steady-state error indicators, such as the ITAE, and in accordance with the performance requirements of the actual control system, appropriate weighting of each index item. Use with base and improved particle swarm algorithm for multi-objective optimization PID. PSO optimization algorithm has good global search ability and high convergence rate. You can respond to the PID controller tuning parameters directly from the output of the PID control system easily balance between speed and stability control systems. After using PSO-PID control in two regional power system ，the amount of overshoot in the step total response downs from 65% to 5%. Adjust time downs from 12 seconds to 3 seconds. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6835

Optimization of PI Control Parameters for Shunt Active Power Filter Based on PSO

This paper presents a method based improved particle swarm optimization optimizing PI control parameters. When the load changes, parallel active power filter, in the traditional PI control strategies, can not well control DC voltage fluctuations leading the deterioration of its dynamic performance. This paper proposes the improved particle swarm algorithm by increasing symbolic function of inertia weight parameter in particle swarm algorithm, and changing position of the random number in the speed update equation. The improved particle swarm algorithm can avoid "premature" or fall into local optimal solution, so it could get a more accurate solution set. Using improved particle swarm algorithm to solve PI controller optimization objective function, to get a more accurate PI control parameters. Simulation and experimental results show that mutations in the case load, the overshooting of an capacitor DC voltage compare with the traditional PI control reduces 5.5%, compared with Particle Swarm Optimization reduces 1.5%. Based on the simulation analyses and experimental results, it has been proved that this method can realize a comprehensive optimization.

Analysis of the Reactive Power Control Strategy Based on Improved Particle Swarm Algorithm

According to the number of constraints of the reactive power control equipment, this paper considers the constraints of the voltage in the load of extreme points of the segmentation program period, and uses dynamic optimization of sub-segmentation in the load curve. It solves the optimization based on applying improved particle swarm optimization into load curve segments, and uses particle swarm optimization through adaptive inertia weight and acceleration factor, phase initialization and mutation operations in order to improve the ability of global optimization.

A Clustering Algorithm Based on Improved Particle Swarm Optimization

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

An Improved Particle Swarm Optimization Based on Biological Chemotaxis

Standard particle swarm algorithm for function optimization prone to local optimal and premature convergence, and thus the biological chemotaxis principle introduction to particle swarm optimization algorithm, this paper proposed an improved algorithm to maintain the diversity of the populationand the choice of key parameters. Simulation results show that, compared with the traditional particle swarm optimization algorithm, an improved particle swarm algorithm for dealing with complex multimodal function optimization problem can be significantly improved algorithm for global optimization.

A multi-agent-based energy-coordination control system for grid-connected large-scale wind–photovoltaic energy storage power-generation units

A multi-agent-based energy-coordination control system (MA-ECCS) is designed for grid-connected large-scale wind–photovoltaic energy storage power-generation units (WPS-PGUs) to address the challenges of low operation efficiency, poor stability, and complex decision making. The proposed system adopts the negotiation model of the contract net protocol with the non-fixed client–server cooperative mechanism among agents. When the power supply and demand are imbalanced, the system selects the state-changeable agent as the task initiator according to the load balance principle. In the energy-coordination process, the proposed system solves the global optimal-energy distribution plan of the system by considering the self-constraint and control objective of each agent and by using the improved particle swarm algorithm to achieve the maximum economic benefit on the basis of stable operations. The paper presents the overall design idea of the system, the agent models of different equipment, and the global energy-coordination optimization algorithm. An example is given to discuss the behavior characteristics of the agent and communication negotiation process and to verify that the system designed in this paper can realize the energy-coordination dispatching flexibly and efficiently. The method adopted by the system can improve the overall operation efficiency and economic benefits of WPS-PGUs.

Optimal control of steel structures by improved particle swarm

Active control is one of the modern approaches in seismic design of steel structures. Recently, induced by economic considerations, especially high expenses of control systems, optimality has become an important issue. In this paper an active system is used to control a steel structure’s displacements by a simplified pole assignment method. To optimize the number, the locations, and the total driving force of the required actuators, an improved particle swarm algorithm is presented focusing on the parameters of the velocity equation. A Geographical neighborhood topology and an adaptive inertia weight are used to improve the standard PSO algorithm. In addition to the local and global best solutions, the positions of the best particles in the geographical neighborhood are mathematically represented in an additional term. The performance of the proposed algorithm is compared with the traditional Genetic Algorithm (GA) and the standard particle swarm considering the optimal control of a 12-story steel structure as a numerical example. High capabilities of the proposed method in terms of the control target, convergence rate, and accuracy are simultaneously clarified by the results.

A Quantum-behaved Particle Swarm Algorithm on Autogenerating Test Paper

This paper puts forward an improved particle swarm algorithm in order to improve the efficiency of autogenerating test paper. The paper conducts mathematically modeling based on item response theory. In the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

Reactive Power Optimization for Power System with Wind Farm Based on Improved Particle Swarm Algorithm

The problem of power system reactive power optimization is a mathematical problem with multiple variables and constraints, which is complex and non-linear. The control variables include continuous variables and discrete variables; its difficult to get the optimal solution. A solution in power flow calculation is put forward after the mathematical model of wind farm is built in this paper. Based on this, taking the WSCC 9 nodes system as an example, use the particle swarm algorithm to solve the reactive power optimization problem. The result shows that this algorithm has an apparent positive effect on reducing system power loss and improving system voltages.

Optimization Technology of PID Parameter in Control System Based on Improved Particle Swarm Optimization Algorithms

Particle swarm algorithm has been proven to be very good solving many global optimization problems. Firstly we improved particle swarm optimization algorithm, the improved PSO algorithm for continuous optimization problem, in solving the nonlinear combinatorial optimization problems and mixed integer nonlinear optimization problems is very effective. This design adopts the improved particle swarm algorithm to optimize the PID parameters of the control system, and the effectiveness of the improved algorithm is proved by experiment.

Trains Trouble Shooting Based on Wavelet Analysis and Joint Selection Feature Classifier

According to urban train running status, this paper adjusts constraints, air spring and lateral damper components running status and vibration signals of vertical acceleration of the vehicle body, combined with characteristics of urban train operation, we build an optimized train operation adjustment model and put forward corresponding estimation method-- wavelet packet energy moment, for the train state. First, we analyze characteristics of the body vertical vibration, conduct wavelet packet decomposition of signals according to different conditions and different speeds, and reconstruct the band signal which with larger energy; we introduce the hybrid ideas of particle swarm algorithm, establish fault diagnosis model and use improved particle swarm algorithm to solve this model; the algorithm also gives specific steps for solution; then calculate features of each band wavelet packet energy moment. Changes of wavelet packet energy moment with different frequency bands reflect changes of the train operation state; finally, wavelet packet energy moments with different frequency band are composed as feature vector to support vector machines for fault identification

Construction Engineering Cost Evaluation Model and Application Based on RS-IPSO-BP Neural Network

Aimed at coping with the complexity of construction engineering cost evaluation, the advantages of rough set theory, particle swarm algorithm and BP neural network are integrated to put forward a new model of construction engineering cost evaluation, namely, the model of construction engineering cost evaluation of optimized particle swarm and BP neural network on the basis of rough set theory. First, rough set theory was used to reduce the factors affecting construction engineering cost and optimize input variables of BP neural network. Then, the improved particle swarm algorithm with constriction factors is adopted to optimize the initial weights and thresholds. Through this method, BP neural network can be used in a better way to solve nonlinear problems and to improve the rate of convergence and the ability to search global optimum. An engineering project in a city of Hunan is selected to make empirical analysis. It shows that based on the features of engineering, this new model enjoys a high practical value as it can be applied to make scientific evaluation of costs of construction engineering.

Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.