Software Testing is continuous process of development and maintenance in life of software. In maintenance phase, regression testing gets exercisedwith additional resources/time for performance. The prioritization of test cases helps to reduce the cost-time of regression testing. Hence, completing Regression Testing effectively and on schedule is challenge for software tester. In this research p...

Our main objective in this article is to achieve minimum side lobe levels for a specific first null beam-width and also a minimum size of the circumference by an optimization-based design method for non-uniform, planar, and circular antenna arrays. Our approach is based on a new variant of Particle swarm Optimization technique. This new technique is a hybrid of Local Neighborhood based PSO with...

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...

The purpose of this research is predicting the stock prices using the Particle Swarm Optimization Algorithm and Box-Jenkins method. In this way, the information of 165 corporations is collected from 2001 to 2016. Then, this research considers price to earnings per share and earnings per share as main variables. The relevant regression equation was created using two variables of earnings per sha...

-Aiming at the shortcoming of basic PSO algorithm, that is, easily trapping into local minimum, we propose an advanced PSO algorithm with SA and apply this new algorithm for solving TSP problem. The core of algorithm is based on the PSO algorithm. SA method is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. The comparative experiments were made between PS...

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search...

The process of learning Bayesian networks includes structure learning and parameters learning. During the process, learning the structure of Bayesian networks based on a large database is a NP hard problem. The paper presents a new hybrid algorithm by integrating the algorithms of MMPC (max-min parents and children), PSO (particle swarm optimization) and GA (genetic algorithm) effectively. In t...

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...