Improved Type2-NPCM Fuzzy Clustering Algorithm Based on Adaptive Particle Swarm Optimization for Takagi–Sugeno Fuzzy Modeling Identification

2020 ◽  
Vol 22 (6) ◽  
pp. 2011-2024
Author(s):  
Lassad Houcine ◽  
Mohamed Bouzbida ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Fuzzy Modeling ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Fuzzy Clustering Algorithm ◽  
Takagi Sugeno

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

2010 ◽  
Vol 44-47 ◽  
pp. 4067-4071 ◽  
Author(s):  
Xue Yong Li ◽  
Jia Xia Sun ◽  
Jun Hui Fu ◽  
Guo Hong Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Space ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy Clustering Algorithm

A fuzzy clustering algorithm based on improved particle swarm optimization was proposed in this paper. First reduce dimension of solution space, separate it into smaller solution space. In separated solution space, use of improved particle swarm optimization algorithm to search the sub-optimal solution as a chromosome of whole particle,use improved PSO to search global optimal solution. The particle solve the problem that swarm algorithm easy to fall into local optimal solution in high dimensional space, and the problem that the fuzzy clustering algorithm is sensitive to initial value problems. Simulation results show the effectiveness of this algorithm.

scholarly journals A Novel Fuzzy Clustering Recommendation Algorithm Based On Pso

2014 ◽  
Vol 14 (5) ◽  
pp. 108-117 ◽  
Author(s):  
Zhang Hui ◽  
You Fei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Search Space ◽  
Improved Method ◽  
Recommendation Algorithm ◽  
Swarm Optimization ◽  
Fuzzy Clustering Algorithm

Abstract Aiming at the problem of recommendation systems, this paper proposes a fuzzy clustering algorithm based on particle swarm optimization. This algorithm can find the best solution, using the capacity of global search in PSO algorithm with a powerful global and defining a proportion factor, which can adjust the position and reduce the search space automatically. Then using mutation particles it replaces the particles flying out the solution space by new particles during the searching process. In order to check the performance of the proposed algorithm, by testing with typical ZDT1, ZDT2, ZDT3 functions, the experimental results show that the improved method not only has a better ability to converge to the global point, but can also efficiently avoid premature convergence.

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