Durbadal Mandal

TL;DR: Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA, PSO and SA applied to the same problem.

TL;DR: APSO is an improved version of conventional PSO in which estimation of evolution state determines one of the four phases such as exploration, exploitation, convergence and jump-out for the entire optimization process that brings better balance in exploration and exploitation stages in multidimensional search space that leads to near global optimal solution.

TL;DR: The obtained simulation results confirm the efficacy and robustness of the proposed identification method in terms of the convergence speed, computational time and various performance metrics as compared to KF, Kalman smoother (KS) which is optimised using different state-of-the-art evolutionary algorithms and also other existing recently reported similar types of stochastic algorithms based approaches.

TL;DR: The simulation results show that the number of effective antenna elements can be brought down from 279 to 139 with simultaneous reduction in Side Lobe Level by 20.37 dB relative to the main beam with a fixed half power beamwidth using PSO.