chaotic pso with mutation for classification

Chaotic Particle Swarm Optimization with Mutation for Classification

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation ...

Improvement on PSO with Dimension Update and Mutation

Sub-dimension particle swarm optimization(s-dPSO) is proposed based on basic particle swarm optimization (bPSO). Each dimension of particle in s-dPSO is updated in turn. The dimensions with poor diversity would be mutated that is initialized again to improve the diversity of population and get global optimal solution when the algorithm is in the local convergence. Most Benchmark function get go...

Multi-channel Diffusion Tensor Image Registration via adaptive chaotic PSO

Registration or spatial normalization of diffusion tensor images plays an important role in many areas of human brain white matter research, such as analysis of Fraction Anisotropy (FA) or whiter matter tracts. More difficult than registration of scalar images, spatial normalization of tensor images requires two important parts: one is tensor interpolation, and the other is tensor reorientation...

PSO Optimized Multipurpose Image Watermarking Using SVD and Chaotic Sequence

Design of PSO-based Fuzzy Classification Systems

In this paper, a method based on the particle swarm optimization (PSO) is proposed for pattern classification to select a fuzzy classification system with an appropriate number of fuzzy rules so that the number of incorrectly classified patterns is minimized. In the PSO-based method, each individual in the population is considered to automatically generate a fuzzy classification system for an M...

PSO optimized Feed Forward Neural Network for offline Signature Classification

The paper is based on feed forward neural network (FFNN) optimization by particle swarm intelligence (PSI) used to provide initial weights and biases to train neural network. Once the weights and biases are found using Particle swarm optimization (PSO) with neural network used as training algorithm for specified epoch, the same are used to train the neural network for training and classificatio...