scholarly journals Multivariable PID Decoupling Control Method of Electroslag Remelting Process Based on Improved Particle Swarm Optimization (PSO) Algorithm

Information ◽  
2014 ◽  
Vol 5 (1) ◽  
pp. 120-133 ◽  
Author(s):  
Jie-Sheng Wang ◽  
Chen-Xu Ning ◽  
Yang Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Control Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electroslag Remelting ◽  
Decoupling Control ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 394 ◽  
pp. 505-508 ◽  
Author(s):  
Guan Yu Zhang ◽  
Xiao Ming Wang ◽  
Rui Guo ◽  
Guo Qiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Genetic Mutation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Optimum Path ◽  
Improved Pso

This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

CDMA Multiuser Detection Based on Improved Particle Swarm Optimization Algorithm

2011 ◽  
Vol 50-51 ◽  
pp. 3-7 ◽  
Author(s):  
Nan Ping Liu ◽  
Fei Zheng ◽  
Ke Wen Xia
Keyword(s):  
Particle Swarm Optimization ◽  
Multiuser Detection ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Discrete Pso ◽  
Case Simulation ◽  
Multi Access

CDMA multiuser detection (MUD) is a crucial technique to mobile communication. We adopt improved particle swarm optimization (PSO) algorithm in MUD which incorporates factor and utilizes function to discrete PSO. Comparison of BER and near-far effect has verified its effectiveness on multi-access interference (MAI). The algorithm accelerates the convergent speed meanwhile it also displays feasibility and superiority in case simulation.

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.

Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy

2014 ◽  
Vol 1049-1050 ◽  
pp. 1654-1657
Author(s):  
Jie Liu ◽  
Xu Sheng Gan ◽  
Wen Ming Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Parameters Selection ◽  
Sharing Strategy ◽  
Selection Of

To optimize the parameters of LS-SVM effectively, an improved Particle Swarm Optimization (PSO) algorithm is proposed to select the optimal parameters combination. For the improvement of the precocity in PSO algorithm, an multi-particles sharing strategy is introduced in simple PSO algorithm to enhance the convergence. The simulation indicates that the proposed PSO algorithm has a better selection on LS-SVM parameters.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Heat Transfer Enhancement Across a Pair of Confined Cylinders Using Improved Particle Swarm Optimization Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi ◽  
Ehsan Mirzakhalili
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Pso Algorithm ◽  
Transformation Method ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The dissipation of the heat generated by electronic devices is the key issue in design and development of such products. The trend, especially in the computer industries, has been reducing the size and increasing the computing power of the electronic elements. Studies have indicated that the thermal performance of a micro-channel depends on its geometric parameters and flow conditions. Many techniques have been developed to enhance the performance of confined elliptical cylinders while minimizing the momentum loss. In this paper, a new robust optimization technique is presented. This new technique is an improved Particle Swarm Optimization (PSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests. The case study in this paper is a two dimensional incompressible flow of non-Newtonian power-law fluid over a pair of elliptical tandem cylinders confined in a channel. A new curve parameterization named Class-Shape-Refinement-Transformation method is used to modify the shape of the confined cylinders, and its control points are adopted as the design variables. Furthermore, final solutions obtained from the Territorial Particle Swarm Optimization algorithm reveal an evident improvement over the test case cylinder across all objective functions presented.

scholarly journals A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

2020 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico ◽  
Teresa Ludermir
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Color Image ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Image Problems

In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.

An Improved Particle Swarm Optimization Deployment for Wireless Sensor Networks

2014 ◽  
Vol 18 (2) ◽  
pp. 107-112 ◽  
Author(s):  
Shuxin Ding ◽  
◽  
Chen Chen ◽  
Jie Chen ◽  
Bin Xin
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Rapid Deployment ◽  
Short Time

This paper addresses the issues associated with deployment of sensors, which are critical in wireless sensor networks. This paper provides an improved particle swarm optimization (PSO) algorithm by changing the basic form of PSO and introducing disturbance (d-PSO). By comparing with other PSO-based algorithms, simulation results show that the d-PSO algorithm provides a good-coverage solution with a satisfying coverage rate in a short time. This feature is especially useful for the rapid deployment of sensors.

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