scholarly journals PID Parameters Optimization Research for Hydro Turbine Governor by an Improved Fuzzy Particle Swarm Optimization Algorithm

2016 ◽  
Vol 10 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Chen Gonggui ◽  
Du Yangwei ◽  
Guo Yanyan ◽  
Huang Shanwai ◽  
Liu Lilan
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Parameters Optimization ◽  
Simulation Experiments ◽  
Swarm Optimization

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the standard particle swarm optimization (PSO) algorithm can speed up the convergence speed and improve convergence precision with combination of the fuzzy control thought and the crossover thought in genetic algorithm (GA). The fuzzy control is employed to get better dynamics of balance between global and local search capabilities, and the crossover operator is introduced to enhance the diversity of particles. Two different types of WTRS systems are built and analyzed in the simulation experiments. Furthermore, the sum of regulating time and another number that is the integral of sum for absolute value of system error and the squared governor output signal is considered as the fitness function of this algorithm. The simulation experiments for parameter optimization problem of WTRS system are carried out to confirm the validity and superiority of the proposed IFPSO, as compared to standard PSO, Ziegler Nichols (ZN) algorithm and fuzzy PID algorithm in terms of parameter optimization accuracy and convergence speed. The simulation results reveal that IFPSO significantly improves the dynamic performance of system under all of the running conditions.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

scholarly journals Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 160 ◽  
Author(s):  
Jiawei Jiang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Yakun Lv ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Random Parameters ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Fitness Value ◽  
The Relationship ◽  
Jamming Effect

Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

PID Parameters Optimization of Pressure and Phear Testing Controller Based on CLPSO Algorithm

2011 ◽  
Vol 88-89 ◽  
pp. 104-109
Author(s):  
Hong Ji Wu ◽  
Zhen Qiu Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Hydraulic System ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Swarm Optimization ◽  
New Learning ◽  
Simulation Results ◽  
System Controller

This paper presents an improved algorithm which is Comprehensive Learning Particle Swarm Optimization(CLPSO).CLPSO utilizes a new learning strategy have achieve the goal to accelerate the convergence of the classical PSO.CLPSO algorithm is effective to optimize PID controller’s parameter.The simulation results show its better performance than traditional ways in the PID parameter optimization of a hydraulic system controller.

scholarly journals Task-Scheduling Based on Multi-Objective Particle Swarm Optimization in Spatial Crowdsourcing

2019 ◽  
Vol 8 (1) ◽  
pp. 45-57
Author(s):  
Afra A. Alabbadi and Maysoon F. Abulkhair Afra A. Alabbadi and Maysoon F. Abulkhair
Keyword(s):  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Physical World ◽  
Spatial Crowdsourcing ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Future Work

As a result of the rapid growth of internet and smartphone technology, a novel platform that attracts individuals and groups known as crowdsourcing emerged. Crowdsourcing is an outsourcing platform that facilitates the accomplishment of costly tasks that consume long periods of time when traditional methods are used. Spatial crowdsourcing (SC) is based on location; it introduces a new framework for the physical world that enables a crowd to complete spatialtemporal tasks. The primary issue in SC is the assignment and scheduling of a set of available tasks to a set of proper workers based on different factors, such as the location of the task, the distance between task location and hired worker location, temporal conditions, and incentive rewards. In the real-world, SC applications need to optimize multi-objectives simultaneously to exploit the utility of SC, and these objectives can be in conflict. However, there are few studies that address this multi-objective optimization problem within a SC environment. Thus, the authors propose a multi-objective task scheduling optimization problem in SC that aims to maximize the number of completed tasks, minimize total travel cost, and ensure worker workload balance. To solve this problem, we developed a method that adapts the multi-objective particle swarm optimization (MOPSO) algorithm based on a proposed novel fitness function. The experiments were conducted with both synthetic and real datasets; the experimental results show that this approach provides acceptable initial results. As future work, we plan to improve the effectiveness of our proposed algorithm by integrating a simple ranking strategy based on task entropy and expected travel costs to enhance MOPSO performance.

scholarly journals New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

2020 ◽  
Vol 12 (2) ◽  
pp. 168781402090425 ◽  
Author(s):  
Nguyễn Huy Trưởng ◽  
Dinh-Nam Dao
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Mean Square Displacement ◽  
Particle Swarm ◽  
Mean Square ◽  
Stiffness Parameter ◽  
Swarm Optimization ◽  
Multi Objective

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

The Complex Blind Deflation Algorithm Based Particle Swarm Optimization with Survival of the Fittest Mechanism

2013 ◽  
Vol 347-350 ◽  
pp. 2656-2660
Author(s):  
Wei Zhao ◽  
Chun Peng Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Blind Source Separation ◽  
Simulation Experiment ◽  
Fitness Function ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Active Object ◽  
Swarm Optimization ◽  
Echo Detection ◽  
Survival Of The Fittest

For multi-constraint nonlinear optimization, this paper puts forward a complex blind deflation algorithm based particle swarm optimization with survival of the fittest mechanism (CBD-PSOSFM) which has faster convergence speed, and then gives a quantificational formula of the improved convergence speed, discusses implement method and the rule of parameters design; Because of the blind source separation (BSS) optimization characteristic in nature, the algorithm can be used to implement semi-BSS with nonlinear multi-constraint. For active object echo detection, the paper sets up fitness function with the multi-constraint like as kurtosis, energy and outline and forms the complex blind deflation algorithm. Finally, the simulation experiment of blind deflation to complex echo validates the algorithms validity and faster convergence capability.

Parameter Optimization of Street-Phelps Model Based on Particle Swarm Optimization

2013 ◽  
Vol 710 ◽  
pp. 647-650
Author(s):  
Bi Zhang ◽  
Jia Yang Wang ◽  
Zuo Yong Li
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Global Optimization Problem ◽  
Swarm Optimization ◽  
Adjustment Method ◽  
Model Based

Particle swarm optimization (PSO) is introduced and tried to optimize the parameters of Street-Phelps model. Here, the inertial adjustment method was used to adopt the inertial weight of PSO. Parameters of Street-Phelps model were optimized by PSO, the performance is compared with other method. Results show that PSO plays an important role in solving global optimization problem, and demonstrate the effectiveness and higher accuracy than other methods.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

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