Comparison between NARX parameter estimation methods with Binary Particle Swarm Optimization-based structure selection method

2010 ◽  
Author(s):  
Ihsan M. Yassin ◽  
Mohd N. Taib ◽  
Azlee Zabidi ◽  
Hasliza Abu Hassan ◽  
Husna Zainol Abidin
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Method ◽  
Estimation Methods ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Structure Selection ◽  
Parameter Estimation Methods

scholarly journals A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Naeimeh Elkhani ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Membrane Computing ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
P System ◽  
Processing Unit ◽  
Swarm Optimization

Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.

scholarly journals Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

2019 ◽  
Vol 27 (1) ◽  
pp. 171-183
Author(s):  
Ali Hakem Jabor ◽  
Ali Hussein Ali
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Selection Algorithm ◽  
Swarm Optimization

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization). 

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