Particle swarm optimization (PSO) is one of the most famous swarm-based optimization
techniques inspired by nature. Due to its properties of flexibility and easy implementation …

Nature is a great source of inspiration for solving complex problems in real-world. In this
paper, a hybrid nature-inspired algorithm is proposed for feature selection problem …

Nearest prototype methods can be quite successful on many pattern classification problems.
In these methods, a collection of prototypes has to be found that accurately represents the …

A particle swarm optimization based simultaneous learning framework for clustering and
classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm …

In robot control, rule discovery for understanding of data is of critical importance. Basically,
understanding of data depends upon logical rules, similarity evaluation and graphical …

Acceleration coefficients are the key parameters of particle swarm optimization (PSO)
algorithm used to control the movement of particles by modifying its cognitive and social …

K nearest neighbor classification is one of the most successfully used methods in pattern
recognition. Despite its simplicity and effectiveness, it suffers from several shortcomings …

This paper presents a new approach to Particle Swarm Optimization, called Michigan
Approach PSO (MPSO), and its application to continuous classification problems as a …

Nowadays, the massive growth of data makes the data classification a challenging task. The
feature selection is a demanding area to take this challenge and produce the higher …

Abstract Coalitional Resource Games (CRGs) are a natural and formal framework in which
agents wish to form coalitions to pool their scarce resources in order to achieve a set of …