A Novel Hybrid Binary PSO Algorithm

2011 ◽  
pp. 93-100 ◽  
Author(s):  
Muhammd Ilyas Menhas ◽  
MinRui Fei ◽  
Ling Wang ◽  
Xiping Fu
Keyword(s):  
Pso Algorithm ◽  
Binary Pso

Binary PSO algorithm assisted to investigate the optical sensor based plasmonic nano-bi-domes

Optik ◽  
2016 ◽  
Vol 127 (19) ◽  
pp. 7670-7675 ◽  
Author(s):  
Rasul Keshavarzi ◽  
Majid Akhlaghi ◽  
Farzin Emami
Keyword(s):  
Optical Sensor ◽  
Pso Algorithm ◽  
Binary Pso

A Rough Based Hybrid Binary PSO Algorithm for Flat Feature Selection and Classification in Gene Expression Data

2017 ◽  
Vol 4 (3) ◽  
pp. 341-360 ◽  
Author(s):  
Suresh Dara ◽  
Haider Banka ◽  
Chandra Sekhara Rao Annavarapu
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Pso Algorithm ◽  
Expression Data ◽  
Binary Pso

Lines Loading Optimization in Transmission Expansion Planning Based on Binary PSO Algorithm

2012 ◽  
Vol 1 (1) ◽  
pp. 24-32
Author(s):  
M Mahdavi ◽  
◽  
Hassan Monsef ◽  
Amir Bagheri ◽  
◽  
...  
Keyword(s):  
Pso Algorithm ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Binary Pso

scholarly journals Optimal Performance of a Nonlinear Gantry Crane System via Priority-based Fitness Scheme in Binary PSO Algorithm

2013 ◽  
Vol 53 ◽  
pp. 012011 ◽  
Author(s):  
Hazriq Izzuan Jaafar ◽  
Nursabillilah Mohd Ali ◽  
Z Mohamed ◽  
Nur Asmiza Selamat ◽  
Amar Faiz Zainal Abidin ◽  
...  
Keyword(s):  
Pso Algorithm ◽  
Optimal Performance ◽  
Gantry Crane ◽  
Binary Pso

Reconstruction of Distribution Network Based on Binary PSO and Optimal Flow Method

2013 ◽  
Vol 328 ◽  
pp. 271-275 ◽  
Author(s):  
Shuo Liu ◽  
Biao Liu ◽  
Yin Jun Peng
Keyword(s):  
Optimization Problems ◽  
Distribution Network ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Population Based ◽  
Optimal Flow ◽  
Optimal Network ◽  
Power Loss Reduction ◽  
Optimal Subspace ◽  
Binary Pso

This paper introduces an algorithm based on improved binary PSO and optimal flow to solve the optimal network reconfiguration problem for power loss reduction. The PSO is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The algorithm solves reconstruction of distribution network through two stages. Firstly, the binary PSO algorithm get the optimal subspace from the global space, secondly the optimal flow algorithm obtain the local optimal network structure in the subspace, finally through the PSO algorithm iterative, from the local optimal solution to search the global optimal solution. The results of calculation example show that the proposed algorithm is correct and fast.

scholarly journals Evolutionary Computation for Feature Selection in Classification

2021 ◽  
Author(s):  
◽  
Hoai Nguyen
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Fitness Function ◽  
Pso Algorithm ◽  
Surrogate Models ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Experimental Results ◽  
Multi Objective ◽  
Binary Pso

<p>Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a large feature set containing irrelevant and redundant features, which reduce the classification performance. In order to address the above problem, feature selection is proposed to select a small subset of relevant features. There are three main types of feature selection methods, i.e. wrapper, embedded and filter approaches. Wrappers use a classification algorithm to evaluate candidate feature subsets. In embedded approaches, the selection process is embedded in the training process of a classification algorithm. Different from the other two approaches, filters do not involve any classification algorithm during the selection process. Feature selection is an important process but it is not an easy task due to its large search space and complex feature interactions. Because of the potential global search ability, Evolutionary Computation (EC), especially Particle Swarm Optimization (PSO), has been widely and successfully applied to feature selection. However, there is potential to improve the effectiveness and efficiency of EC-based feature selection.  The overall goal of this thesis is to investigate and improve the capability of EC for feature selection to select small feature subsets while maintaining or even improving the classification performance compared to using all features. Different aspects of feature selection are considered in this thesis such as the number of objectives (single-objective/multi-objective), the fitness function (filter/wrapper), and the searching mechanism.  This thesis introduces a new fitness function based on mutual information which is calculated by an estimation approach instead of the traditional counting approach. Results show that the estimation approach works well on both continuous and discrete data. More importantly, mutual information calculated by the estimation approach can capture feature interactions better than the traditional counting approach.  This thesis develops a novel binary PSO algorithm, which is the first work to redefine some core concepts of PSO such as velocity and momentum to suit the characteristics of binary search spaces. Experimental results show that the proposed binary PSO algorithm evolve better solutions than other binary EC algorithms when the search spaces are large and complex. Specifically, on feature selection, the proposed binary PSO algorithm can select smaller feature subsets with similar or better classification accuracies, especially when there are a large number of features.  This thesis proposes surrogate models for wrapper-based feature selection. The surrogate models use surrogate training sets which are subsets of informative instances selected from the training set. Experimental results show that the proposed surrogate models assist PSO to reduce the computational cost while maintaining or even improving the classification performance compared to using only the original training set.  The thesis develops the first wrapper-based multi-objective feature selection algorithm using MOEA/D. A new decomposition strategy using multiple reference points for MOEA/D is designed, which can deal with different characteristics of multi-objective feature selection such as highly discontinuous Pareto fronts and complex relationships between objectives. The experimental results show that the proposed algorithm can evolve more diverse non-dominated sets than other multi-objective algorithms.   This thesis introduces the first PSO-based feature selection algorithm for transfer learning. In the proposed algorithm, the fitness function uses classification performance to reduce the differences between domains while maintaining the discriminative ability on the target domain. The experimental results show that the proposed algorithm can select feature subsets which achieve better classification performance than four state-of-the-art feature-based transfer learning algorithms.</p>

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