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.

Parameter Optimization of BOD Water Quality Model Based on the Immunity Taboo Search

2012 ◽  
Vol 198-199 ◽  
pp. 839-842
Author(s):  
Jia Yang Wang ◽  
Zuo Yong Li ◽  
Bi Zhang ◽  
Chang Wu Zou
Keyword(s):  
Water Quality ◽  
Global Optimization ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Water Quality Model ◽  
Global Optimization Problem ◽  
Quality Model ◽  
Taboo Search ◽  
Model Based

A new version of Taboo Search (TS), namely, Immunity Taboo Search (ITS) is first introduced and tried to optimize the parameters of BOD water quality model. Here, Taboo Search was improved by Immune Arithmetic (IEA). Parameters of BOD water quality model were optimized by ITS, the performance is compared with other method. Results show that ITS plays an important role in solving global optimization problem, and demonstrate the effectiveness and higher accuracy than other methods.

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.

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.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

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