A Novel Group-based Particle Swarm Optimization-Pattern Search Algorithm for AGC Parameters Optimization with Wind Power Integration

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.

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Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm

IEEE Access ◽

10.1109/access.2021.3072993 ◽

2021 ◽

Vol 9 ◽

pp. 60865-60879

Author(s):

Liu Yang ◽

Zhen Li ◽

Dongsheng Wang ◽

Hong Miao ◽

Zhaobin Wang

Keyword(s):

Particle Swarm Optimization ◽

Search Algorithm ◽

Particle Swarm ◽

Software Defects ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization

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Parameters optimization study and analysis of PID controller in buck converter based on fuzzy particle swarm optimization algorithm

2014 International Conference on Information Science, Electronics and Electrical Engineering ◽

10.1109/infoseee.2014.6946183 ◽

2014 ◽

Cited By ~ 3

Author(s):

Anna Wang ◽

Zhanjun Huang ◽

Cui Lei ◽

Zhang Chao ◽

Erlei Wu

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Pid Controller ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Buck Converter ◽

Parameters Optimization ◽

Swarm Optimization ◽

Optimization Study

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Method of Optimization for Target Localization Model Parameters Based on LSSVR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.934 ◽

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.

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Design Optimization for Fir-Tree Root of Turbine Blade Considering Manufacturing Variations

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.2733 ◽

2013 ◽

Vol 694-697 ◽

pp. 2733-2737

Author(s):

Qin Zhou ◽

Ming Hui Zhang ◽

Hui Yong Chen ◽

Yong Hui Xie

Keyword(s):

Particle Swarm Optimization ◽

Turbine Blade ◽

Equivalent Stress ◽

Particle Swarm ◽

Pattern Search ◽

Design System ◽

Contact Method ◽

Swarm Optimization ◽

Maximum Equivalent Stress ◽

Manufacturing Variation

An optimization design system for fir-tree root of turbine blade has been developed in this paper. In the system, a parametric model of the blade and rim was established based on the parametric design language APDL, and nonlinear contact method was used for analysis by ANSYS, meanwhile some optimization algorithms, such as Pattern Search Algorithm, Genetic Algorithm, Simulated Annealing Algorithm and Particle Swarm Optimization, were adopted to control the optimizing process. Five cases of manufacturing variation in contact surfaces between root and rim were taken into account, and the design objective was to minimize the maximum equivalent stress of root-rim by optimizing eight critical geometrical dimensions of the root and rim. As a result, the maximum equivalent stress of root-rim decreases markedly after the optimization in all cases. In consideration of both precision and computing time, particle swarm optimization is assessed as the best algorithm to solve structure optimization problem in this work. Corresponding to five different cases of manufacturing variation, the maximum equivalent stress of root and rim reduces by 7%, 8%; 27%, 24%; 27%, 22%; 25%, 19%; 10%, 14% using the Particle Swarm Optimization.

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