scholarly journals Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xun Zhang ◽  
Juelong Li ◽  
Jianchun Xing ◽  
Ping Wang ◽  
Qiliang Yang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Sensor Placement ◽  
Particle Swarm ◽  
Optimal Placement ◽  
Optimal Sensor Placement ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Comparison Results ◽  
Ipso Algorithm

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor

2011 ◽  
Vol 130-134 ◽  
pp. 1938-1942
Author(s):  
Xia Bo Shi ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Motor Speed ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ipso Algorithm

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.

scholarly journals Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Houxian Zhang ◽  
Zhaolan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Large Scale Network ◽  
Scale Network

No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

scholarly journals Enhancing Power Quality in PV-SOFC Microgrids Using Improved Particle Swarm Optimization

2019 ◽  
Vol 9 (5) ◽  
pp. 4616-4622
Author(s):  
V. V. Prabhakaran ◽  
A. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Power Quality ◽  
Electricity Markets ◽  
Large Scale ◽  
Control Strategies ◽  
Particle Swarm ◽  
Voltage Source ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The concept of hybrid microgrid (MG) has attracted tremendous attention in modern electricity markets, owing to the enhanced efficiency and reliability it offers to the main electricity grid. Numerous meritorious aspects associated with hybrid MGs are the key features of future large scale renewable technologies. In this paper, a hybrid MG using PV-SOFC (PhotoVoltaic – Solid Oxide Fuel Cell) is connected to an infinite bus bar, in order to achieve an autonomous working mode. The dynamic and steady-state operation with control strategies for both PV and SOFC power systems are analyzed. The objective is to control the voltage and frequency of the MG when it is not connected to the main grid. Typically, an efficient control strategy must assess the power conversion system and its state, in the isolated MG. Moreover, it must reliably handle variant and intermittent type of loads. With this viewpoint, we propose a Voltage Source Inverter (VSI) based Proportional Integral (PI) controller, optimized by Improved Particle Swarm Optimization (IPSO) for the purpose of smooth power flow control improving power quality. The performance of PI-IPSO and PI technologies are evaluated, for the proposed MG, in MATLAB/Simulink. The results obtained verify the effectiveness of the modified PSO algorithm, in comparison to the conventional PI techniques, for the frequency and voltage control of the MG.

scholarly journals An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shouwen Chen ◽  
Zhuoming Xu ◽  
Yan Tang ◽  
Shun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization ◽  
Inertia Weight ◽  
Ipso Algorithm

Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm with BPSO, EPSO, CPSO, and ACL-PSO algorithms, experimental results show that the proposed IPSO algorithm is more efficient; it also outperforms other four baseline PSO algorithms in accuracy.

An Improved PSO-ACO Algorithm for Solving Large-Scale TSP

2010 ◽  
Vol 143-144 ◽  
pp. 1154-1158 ◽  
Author(s):  
Ai Jia Ouyang ◽  
Yong Quan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Shortest Paths ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

scholarly journals Gravity field inversion using Improved Particle Swarm Optimization (IPSO) for estimation of sedimentary basin basement depth

2020 ◽  
Vol 50 (3) ◽  
pp. 303-323
Author(s):  
Soudeh LONI ◽  
Mahmoud MEHRAMUZ
Keyword(s):  
Particle Swarm Optimization ◽  
Sedimentary Basin ◽  
Gravity Data ◽  
Particle Swarm ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Basement Depth ◽  
Optimum Solution ◽  
Ipso Algorithm

In this paper, for the first time an Improved Particle Swarm Optimization (IPSO) algorithm, is developed to evaluate the 2.5-D basement of sedimentary basin and consequently to simulate its bottom, by using the density contrast that varies parabolically with depth simultaneously. The IPSO method is capable of improving the global search of particles in all of the search fields. Finding the optimum solution is adjusted by an inertia weight and acceleration coefficients. Here, we have examined the ability of the IPSO inversion by the synthetic gravity data due to a sedimentary basin, with and without noise. The calculated depth and gravity of the synthetic model do not differ too much from assumed values due to set limits for model parameters and are always within the range. Also, the mentioned method has been applied for the 2.5-D gravity inverse modelling of a sedimentary basin in Iran. We also have modelled the sedimentary basin in 2-D along seven profiles. Furthermore, using the depth values estimated by IPSO from all profiles, a 3-D model of the sedimentary basin was generated. The obtained maximum depth for this sedimentary basin is 2.62 km.

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