A Review of Particle Swarm Optimization: Feature Selection, Classification and Hybridizations

Particle swarm optimization (PSO) is a recently grown, popular, evolutionary and conceptually simple but efficient algorithm which belongs to swarm intelligence category. This paper outlines basic concepts and reviews PSO based techniques with their applications to classification and feature selection along with some of the hybridized applications of PSO with similar other techniques. DOI: 10.17762/ijritcc2321-8169.16041

A Hybrid Rough Set – Particle Swarm Algorithm for Image Pixel Classification

This article presents a framework to hybridize the rough set theory with a famous swarm intelligence algorithm known as Particle Swarm Optimization (PSO). The hybrid rough-PSO technique has been used for grouping the pixels of an image in its intensity space. Medical and remote sensing satellite images become corrupted with noise very often. Fast and efficient segmentation of such noisy images (which is essential for their further interpretation in many cases) has remained a challenging problem for years. In this work, we treat image segmentation as a clustering problem. Each cluster is modeled with a rough set. PSO is employed to tune the threshold and relative importance of upper and lower approximations of the rough sets. Davies–Bouldin clustering validity index is used as the fitness function, which is minimized while arriving at an optimal partitioning. 1

Fuzzy systems and unsupervised computing: exploration of applications in biology

In this thesis we will explore the use of fuzzy systems theory for applications in bioinformatics. The theory of fuzzy systems is concerned with formulating decision problems in data sets that are ill-defined. It supports the transfer from a subjective human classification to a numerical scale. In this manner it affords the testing of hypothesis and separation of the classes in the data. We first formulate problems in terms of a fuzzy system and then develop and test algorithms in terms of their performance with data from the domain of the life-sciences. From the results and the performance, we will learn about the usefulness of fuzzy systems for the field, as well as the applicability to the kind of problems and practicality for the computation itself. Computer Systems, Imagery and Medi