Exponential Inertia Weight for Particle Swarm Optimization

2012 ◽  
pp. 83-90 ◽  
Author(s):  
T. O. Ting ◽  
Yuhui Shi ◽  
Shi Cheng ◽  
Sanghyuk Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inertia Weight

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear

scholarly journals Color Feature-Based Object Tracking through Particle Swarm Optimization with Improved Inertia Weight

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1292 ◽  
Author(s):  
Siqiu Guo ◽  
Tao Zhang ◽  
Yulong Song ◽  
Feng Qian
Keyword(s):  
Particle Swarm Optimization ◽  
Object Tracking ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Color Feature ◽  
Inertia Weight ◽  
Feature Based

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

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