An innovative approach of model order reduction using ant colony optimization
In this paper, a generic solution in the area of model order reduction is presented. The proposed method is based on artificial intelligence, which depends on a probabilistic optimization algorithm called Ant Colony. This approach aims to obtain a reduced-order model out of a relatively complex model represented as a transfer function. This reduced order model is obtained in a reasonable run time. It has acceptable error in time domain represented by the unit step time response, and the least error in frequency domain represented by Bode plot compared to other conventional methods in model order reduction regardless the targeted transfer function.
An Improved Ant Colony Algorithm
Artificial ant colony algorithm is new in the evolution computing. The primary study shows it is a better algorithm with robust based population, but it has some shortcomings such as its slow computing speed, and it is easy to fall in local peak in large scale problem. To overcome these deficiencies, an improved ant colony algorithm is designed through abstracting the advantages of particle swarm optimization (PSO).
A COMPARATIVE STUDY ON PERFORMANCE OF FLEXIBLE PAVEMENT ON EXPANSIVE AND NON EXPANSIVE SOILS
In this present work, performance of flexible pavement on expansive soils and non expansive soils is being evaluated through Road Roughness using 5 th Wheel Bump Integrator and Characteristic deflection using BBD test as per IRC: 81on the completed stretch of 100 Km road from Km.799.999 to Km.901.753 of NH - 16 ( old NH - 5) constructed on BOT (Annuity) basis. During the last six years which is analyzed by statistical tools using Excel to correlate the performance results with the material used for the formation of the road. The observations conclude type of sub grade material used plays significant role in determining the performance of road especially in the expansive soils and behaves coherently to the great extent in the non expansive soils.