Cover Image

A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

Algorithms; Artificial intelligence; Beamforming; Benchmarking; Heuristic algorithms; Iterative methods; Learning algorithms; Particle swarm optimization (PSO); Adaptive Beamforming; Gravitational search algorithm (GSA); Gravitational search algorithms; Heuristic optimization algorithms; Minimum var...

Full description

Saved in:
Bibliographic Details
Main Authors: Darzi S., Sieh Kiong T., Tariqul Islam M., Rezai Soleymanpour H., Kibria S.
Other Authors: 55651612500
Format: Article
Published: Elsevier Ltd 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-22648
record_format dspace
spelling my.uniten.dspace-226482023-05-29T14:11:29Z A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming Darzi S. Sieh Kiong T. Tariqul Islam M. Rezai Soleymanpour H. Kibria S. 55651612500 15128307800 55328836300 57189004509 55637259500 Algorithms; Artificial intelligence; Beamforming; Benchmarking; Heuristic algorithms; Iterative methods; Learning algorithms; Particle swarm optimization (PSO); Adaptive Beamforming; Gravitational search algorithm (GSA); Gravitational search algorithms; Heuristic optimization algorithms; Minimum variance distortionless response; Optimal trajectories; Optimization problems; Real-world optimization; Optimization This paper introduces a memory-based version of gravitational search algorithm (MBGSA) to improve the beamforming performance by preventing loss of optimal trajectory. The conventional gravitational search algorithm (GSA) is a memory-less heuristic optimization algorithm based on Newton's laws of gravitation. Therefore, the positions of agents only depend on the optimal solutions of previous iteration. In GSA, there is always a chance to lose optimal trajectory because of not utilizing the best solution from previous iterations of the optimization process. This drawback reduces the performance of GSA when dealing with complicated optimization problems. However, the MBGSA uses the overall best solution of the agents from previous iterations in the calculation of agents� positions. Consequently, the agents try to improve their positions by always searching around overall best solutions. The performance of the MBGSA is evaluated by solving fourteen standard benchmark optimization problems and the results are compared with GSA and modified GSA (MGSA). It is also applied to adaptive beamforming problems to improve the weight vectors computed by Minimum Variance Distortionless Response (MVDR) algorithm as a real world optimization problem. The proposed algorithm demonstrates high performance of convergence compared to GSA and Particle Swarm Optimization (PSO). � 2016 Elsevier B.V. Final 2023-05-29T06:11:29Z 2023-05-29T06:11:29Z 2016 Article 10.1016/j.asoc.2016.05.045 2-s2.0-84982108511 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982108511&doi=10.1016%2fj.asoc.2016.05.045&partnerID=40&md5=f76eb1499a01f385c4be4ef15bd86646 https://irepository.uniten.edu.my/handle/123456789/22648 47 103 118 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Algorithms; Artificial intelligence; Beamforming; Benchmarking; Heuristic algorithms; Iterative methods; Learning algorithms; Particle swarm optimization (PSO); Adaptive Beamforming; Gravitational search algorithm (GSA); Gravitational search algorithms; Heuristic optimization algorithms; Minimum variance distortionless response; Optimal trajectories; Optimization problems; Real-world optimization; Optimization
author2 55651612500
author_facet 55651612500
Darzi S.
Sieh Kiong T.
Tariqul Islam M.
Rezai Soleymanpour H.
Kibria S.
format Article
author Darzi S.
Sieh Kiong T.
Tariqul Islam M.
Rezai Soleymanpour H.
Kibria S.
spellingShingle Darzi S.
Sieh Kiong T.
Tariqul Islam M.
Rezai Soleymanpour H.
Kibria S.
A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
author_sort Darzi S.
title A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
title_short A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
title_full A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
title_fullStr A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
title_full_unstemmed A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
title_sort memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming
publisher Elsevier Ltd
publishDate 2023
_version_ 1806425857305083904
score 13.222552

Similar Items