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...
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2023
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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 |
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| 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 |
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55651612500 Darzi S. Sieh Kiong T. Tariqul Islam M. Rezai Soleymanpour H. Kibria S. |
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Article |
| author |
Darzi S. Sieh Kiong T. Tariqul Islam M. Rezai Soleymanpour H. Kibria S. |
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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 |
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13.214268 |