Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique

Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep Singh Jit

doi:10.1371/journal.pone.0140526

Cited by 6 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GSA algorithm was proposed by Rashedi, et al [ 20 , 28 ] as a global optimization method in 2009. The GSA agent’s movements are estimated through their masses.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…MVDR is a minimum output energy (MOE) system based beamforming technique, as explicitly described previously [ 28 ]. MVDR keeps a distortionless main lobe response towards the wanted signal while simultaneously minimizing the array output power.…”

Section: Mvdr Beamformingmentioning

confidence: 99%

“…In this paper, the ECGSA was employed to enhance the MVDR beamforming technique performance by increasing the SINR value of four element array. The MVDR integrated with ECGSA, GSA and PSO method will optimize the SINR via the complex weights, as described in detail previously [ 28 ].…”

Section: Model Applicationmentioning

confidence: 99%

“…The effectiveness of GSA and its version for binary encoded problems (BGSA) [ 27 ] in solving a set of nonlinear benchmark functions has been proven. However, very few of these GSA variants have been implemented for the beamforming applications [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…The most significant difference between stochastic leader gravitational search algorithm (SLGSA) [ 28 ] and ECGSA is that ECGSA retains memory of the results from previous iterations, whereas, SLGSA is memory less (i.e. uses results from current iteration only).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum

et al. 2016

Self Cite

View full text Add to dashboard Cite

An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.

show abstract

“…The GSA algorithm was proposed by Rashedi, et al [ 20 , 28 ] as a global optimization method in 2009. The GSA agent’s movements are estimated through their masses.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Section: Mvdr Beamformingmentioning

confidence: 99%

Section: Model Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations