Parallel Implementation of Hybrid Gsa-Nm Algorithm for Adaptive Beam-Forming Applications

Abstract

Abstract-Recently researchers have great interest in using multi-core processors for applications requiring intensive parallel computing. In this paper, an approach for the implementation of hybrid parallel Gravitational Search Algorithm (GSA) and Nelder-Mead (NM) algorithm using open MultiProcessing (OPEN-MP) on multi-core processors is proposed for beam-forming applications. The proposed parallel GSA-NM algorithm is used to optimize the complex excitations, amplitudes and phases, of the adaptive array elements to synthesize the array beam-pattern. The array consists of 24-elements uniformly distributed in a circular configuration. To measure the performance of the proposed approach, the results are compared with those obtained using parallel hybrid CFO-NM, and PSO-NM Algorithms.

Performance Enhancement for Adaptive Beam-Forming Application Based Hybrid PSOGSA Algorithm

“…The hybrid PSOGSA is used to adjust the weights of phase shift of the excitation of each element of the UCA array for beam synthesis to maximize the output power toward the desired signals at i θ (scenario #1 two users at: 30 , 0 ). To validate the algorithm, the results are compared to those obtained using GSA which used in [13]. Both algorithms are employed with a population size of 30 and 150 iterations.…”

“…As can be seen in Figure 3, after initialization, evaluate the fitness function based on Equation (1). Gravitational force, gravitational constant, and resultant forces among agents are calculated using Equations (12), (14) and (13) respectively. After that, the accelerations of particles are defined as Equation (11).…”

“…In [12], planar ultra-wide band (UWB) antennas with irregular radiator shapes were designed using GSA and compared with those obtained using CFO algorithm, it was found that the GSA gave better performance than CFO. In [13], GSA was implemented using Open-MP and its results outperformed the PSO by 65.09% in terms of normalized array factor. In [14], an algorithm based on collective animal behaviour (CAB) was used for finding the best optimal non-uniform excitation weights and optimal uniform inter-element spacing for hyper beam-forming of linear antenna arrays.…”