A color image quantization algorithm based on Particle Swarm Optimization (PSO) is developed in this paper. PSO is a population-based optimization algorithm modeled after the simulation of social behavior of bird flocks and follows similar steps as evolutionary algorithms to find near-optimal solutions. The proposed algorithm randomly initializes each particle in the swarm to contain K centroid...

In this paper, an innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy cmean (NFCM) clustering, particle swarm optimization (PSO) and recursive least-squares (RLS) is proposed to generate radial basis function networks (RBFNs) modeling system with small numbers of descriptive radial basis functions (RBFs) for fast approximating two complex and...

In this paper, we propose a novel Spatial Clustering with Obstacles Constraints (SCOC) based on Dynamic Piecewise Linear Chaotic Map and Dynamic Nonlinear Particle Swarm Optimization (PNPSO) and K-Medoids, which is called PNPKSCOC. The contrastive experiments show that PNPKSCOC is effective and has better practicalities, and it performs better than PSO K-Medoids SCOC in terms of quantization er...

We have devised a way of segmentation and progressive transmission of MRI images. Entropy maximization using Particle Swarm Algorithm (PSO) is used to get the Region of Interest (ROI). The ROI is de-noised using Multi-Wavelet Analysis. Soft Thresholding together with Stationary Wavelet is used for de-noising purpose. Varying percentages of Discrete Cosine Transform Coefficients are used for the...

This contribution deals with the hybridization of complex network frameworks and metaheuristic algorithms. The population is visualized as an evolving complex network that exhibits non-trivial features. It briefly investigates the time and structure development of a complex network within a run of selected metaheuristic algorithms – i.e. PSO and Differential Evolution (DE). Two different approa...