Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique

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 s...

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Bibliographic Details
Published in:PloS one 2015-11, Vol.10 (11), p.e0140526-e0140526
Main Authors: Darzi, Soodabeh, Islam, Mohammad Tariqul, Tiong, Sieh Kiong, Kibria, Salehin, Singh, Mandeep
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive search techniques
Algorithms
Antennas
Beamforming
Benchmarks
Convergence
Engineering
Engineering research
Gravitation
Gravity
Immune system
Microwaves
Models, Theoretical
Optimization
Random Allocation
Randomization
Search algorithms
Search Engine - methods
Stochasticity
Wireless Technology
World problems
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0140526