In this paper an extensive empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The algorithm is tested on extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA). Key-Words: Global Optimization, Particle ...

In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based app...

In this paper, two evolutionary algorithmsInvasive Weed Optimization (IWO) based power system stabilizer (PSS) and particle swarm optimization (PSO) based power system stabilizer is designed for multi-machine power system to compare their tuning performances. IWO is a derivative-free real parameter optimization technique that mimics the ecological behavior of colonizing weeds. PSO is also a der...

In this paper, the new hybrid particle swarm optimization (hybrid-PSO) based on particle swarm optimization (PSO), evolutionary programming (EP), and tabu search (TS) is developed. Hybrid-PSO is proposed to determine the optimal allocation of multi-type flexible AC transmission system (FACTS) controllers for simultaneously maximizing the power transfer capability of power transactions between g...

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algori...

Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. Despite their wide-spread use, in this paper, we investigate standard PSO algorithms for their ability to (i) approach the optimal region and (ii) simultaneously focus in order to find the optimum precisely, in simplistic yet scalabl...

In the present study Artificial Neural Network (ANN) has been optimized using a hybrid algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The hybrid GA-PSO algorithm has been used to improve the estimation of electricity demand of the state of Tamil Nadu in India. The ANN-GA-PSO model uses gross domestic product (GSDP); electricity consumption per capita; income growth r...

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can ...

Particle swarm optimization(PSO) has been applied on feature selection with many improved results. Traditional PSO methods have some drawbacks when dealing with binary space, which may have negative effects on the selection result. In this paper, an algorithm based on fitness proportionate selection binary particle swarm optimization(FPSBPSO) will be discussed in detail aiming to overcome the p...

Multilevel thresholding is a method that is widely used in image segmentation. The thresholding problem is treated as an optimization problem with an objective function. In this article, a simple and histogram based approach is presented for multilevel thresholding in image segmentation. The proposed method combines Tsallis objective function and Particle Swarm Optimization (PSO). The PSO algor...