This paper presents a hybrid particle swarm optimization algorithm (HPSO) as a modern optimization tool to solve the optimal power flow (OPF) problem. The objective functions considered are the system real power losses, fuel cost, voltage deviation and voltage stability index. The proposed algorithm makes use of the PSO, known for its global searching capabilities, to allocate the optimal contr...

In this paper a new approach for reducing the side lobe level of amplitude tapered linear array using Chebyshev polynomial and Particle Swarm Optimization (PSO) is presented. The array geometry synthesis is first formulated as an optimization problem with the goal of side lobe level reduction and then solved using PSO algorithm for optimum current excitations. While solving optimization problem...

Grid computing which is based on the high performance computing environment, basically used for solving complex computational demands. In the grid computing environment, scheduling of tasks is a big challenge. The task scheduling problem can be defined as a problem of assigning the number of resources to tasks where number of resources is less than the number of available tasks. Particle swarm ...

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to inc...

In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic alg...

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...

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or...

Particle Swarm Optimization (PSO) is one of the latest nature inspired meta-heuristic algorithms based on the metaphor of social interaction and communication such as bird flocking and fish schooling. PSO is a population based algorithm for finding optimal regions of complex search spaces through interaction of individuals in the population. In PSO, a set of randomly generated solutions (initia...

Image segmentation is an important research issue in image processing. In this paper, hybridizing of the PSO and BBO algorithm for 2-D image segmentation is implemented. The common features from PSO and BBO algorithm are used and then hybridized for the segmentation. The results are evaluated on the basis of parameters; PSNR and SSIM. The results depicts that the proposed hybrid algorithm perfo...