This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via ...

This paper presents asynchronous particle swarm optimization (APSO) applied to the time-domain inverse scattering problems of two-dimensional metallic cylinder buried in slab medium. For this study the finite-difference time-domain (FDTD) is employed for the analysis of the forward scattering part, while for the APSO is applied for the reconstruction of the two-dimensional metallic cylinder bur...

Palette re-ordering is a class of pre-processing method with the objective to manipulate the palette index such that the adjacent symbols are assigned close indices in the symbol space, thus enhancing the compressibility of the image with many lossless compressors. Finding an exact reordered palette would certainly be exhaustive and computationally complex. A solution to this NP hard problem is...

Frequency deviation is a one of major technical issue of a interconnected power system. In this paper an optimized Proportional-Integral-Derivative (PID) controller ios proposed for controlling the frequency deviation. To optimize the gain values of controller, the Adaptive Particle Swarm Optimization (APSO) algorithm is utilized. Tuning of controllers is done in order to get the gain values or...

There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The go...

Abstract An advanced hybrid algorithm ( h aDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) particle swarm (aPSO) integrated with aDEPSO. In aDE a novel, mutation, crossover selection strategy introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, e...