Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

2017 ◽  
Vol 73 (11) ◽  
pp. 4773-4795 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Ahamad Tajudin Khader
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Genetic Operators ◽  
Feature Selection Technique ◽  
Swarm Optimization ◽  
Selection Technique ◽  
Hybrid Particle Swarm Optimization ◽  
Text Feature

scholarly journals Hybrid Particle Swarm Optimization Algorithm for Process Planning

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1745
Author(s):  
Xu Zhang ◽  
Pan Guo ◽  
Hua Zhang ◽  
Jin Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Process Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Genetic Operators ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hierarchical Coding ◽  
Hybrid Particle Swarm Optimization

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

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