In order to accurately, fast and efficiently forecast the short-term load of power system, an improved particle swarm optimization algorithm is proposed to optimize the parameters of fuzzy radial basis function fuzzy neural network(FRBFNN) model in order to train the FRBFNN model for obtaining the optimized FRBFNN(IWPSRFN) method. In the proposed IWPSRFN method, the linear decreasing weight met...

Particle swarm optimization is one of the evolutionary computations which is inspired by social behavior of bird flocking or fish schooling. This research focuses on the application of the particle swarm optimization to two-dimensional packing problem. Packing problem is a class of optimization problems which involve attempting to pack the items together inside a container, as densely as possib...

In this paper an improved particle swarm optimization algorithm (IPSO) for electromagnetic applications is proposed. In order to overcome the drawbacks of standard PSO, some improved mechanisms for velocity updating, the exceeding boundary control, global best perturbation and the simplified quadratic interpolation (SQI) operator are adopted. To show the effectiveness of the proposed algorithm,...

Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We propose...

BACKGROUND Improved understanding of rod fracture (RF) in adult spinal deformity could be valuable for implant design, surgical planning, and patient counseling. OBJECTIVE To evaluate symptomatic RF after posterior instrumented fusion for adult spinal deformity. METHODS A multicenter, retrospective review of RF in adult spinal deformity was performed. Inclusion criteria were spinal deformit...

Block matching algorithm for motion estimation with the concept of two optimization techniques Particle Swarm Optimization (PSO) and Differential Evolution (DE) are carried out. Motion Estimation results shows that the DE algorithm for motion estimation gives improved PSNR value when compared with PSO algorithm.

A boolean particle swarm optimisation (PSO) algorithm is presented that builds on the strengths of earlier proposals but which by introducing a wholly random element into the search process shows greatly improved performance in higher dimensional search spaces in comparison also to the binary PSO algorithm of Kennedy and Eberhart.

Tuning of PID controller parameters is an important problem in control field. To solve this problem we used an Advanced Particle Swarm Optimization which is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. The proposed method has fast searching speed compared to standard PSO. Furthermore this method accelerates the convergence. However...

In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based heuristic search technique used for s...