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Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique.

Darzi S, Islam MT, Tiong SK, Kibria S, Singh M - PLoS ONE (2015)

Bottom Line: Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results.Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence.The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants.

View Article: PubMed Central - PubMed

Affiliation: Center for Space Science (ANGKASA), Universiti Kebangsaan Malaysia, Selangor, Malaysia.

ABSTRACT
In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA.

No MeSH data available.


Comparison of performance of power response for user at 0° with interference at 30°, 50°, 25° and 60° with 100 iterations.(a) MVDR (b) SGSA-MVDR (c) SL-GSA-MVDR.
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pone.0140526.g010: Comparison of performance of power response for user at 0° with interference at 30°, 50°, 25° and 60° with 100 iterations.(a) MVDR (b) SGSA-MVDR (c) SL-GSA-MVDR.

Mentions: The power response for MVDR, SGSA-MVDR and SL-GSA-MVDR is plotted in Fig 10 according to the weights in Table 9 to explain the target user and interference by using different values. Conventional MVDR rarely manages to create sufficiently deep s towards more than 3 interference sources as shown in Fig 10. The two interferences at 25° and 30° produce one shallow for MVDR and SGSA-MVDR. Furthermore, the s towards the other two interference sources are similarly shallow. Two of the three s created by MVDR are significantly improved by the implementation of the proposed algorithm. This clearly shows that SL-GSA can outperform the beamforming techniques in complex scenarios involving more than 3 interfering sources. The deep s correspond to superior SINR performance as shown in Table 10.


Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique.

Darzi S, Islam MT, Tiong SK, Kibria S, Singh M - PLoS ONE (2015)

Comparison of performance of power response for user at 0° with interference at 30°, 50°, 25° and 60° with 100 iterations.(a) MVDR (b) SGSA-MVDR (c) SL-GSA-MVDR.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4638346&req=5

pone.0140526.g010: Comparison of performance of power response for user at 0° with interference at 30°, 50°, 25° and 60° with 100 iterations.(a) MVDR (b) SGSA-MVDR (c) SL-GSA-MVDR.
Mentions: The power response for MVDR, SGSA-MVDR and SL-GSA-MVDR is plotted in Fig 10 according to the weights in Table 9 to explain the target user and interference by using different values. Conventional MVDR rarely manages to create sufficiently deep s towards more than 3 interference sources as shown in Fig 10. The two interferences at 25° and 30° produce one shallow for MVDR and SGSA-MVDR. Furthermore, the s towards the other two interference sources are similarly shallow. Two of the three s created by MVDR are significantly improved by the implementation of the proposed algorithm. This clearly shows that SL-GSA can outperform the beamforming techniques in complex scenarios involving more than 3 interfering sources. The deep s correspond to superior SINR performance as shown in Table 10.

Bottom Line: Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results.Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence.The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants.

View Article: PubMed Central - PubMed

Affiliation: Center for Space Science (ANGKASA), Universiti Kebangsaan Malaysia, Selangor, Malaysia.

ABSTRACT
In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA.

No MeSH data available.