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

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dc.citedby20
dc.contributor.authorDarzi S.en_US
dc.contributor.authorSieh Kiong T.en_US
dc.contributor.authorTariqul Islam M.en_US
dc.contributor.authorRezai Soleymanpour H.en_US
dc.contributor.authorKibria S.en_US
dc.contributor.authorid55651612500en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid55328836300en_US
dc.contributor.authorid57189004509en_US
dc.contributor.authorid55637259500en_US
dc.date.accessioned2023-05-29T06:11:29Z
dc.date.available2023-05-29T06:11:29Z
dc.date.issued2016
dc.descriptionAlgorithms; 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; Optimizationen_US
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asoc.2016.05.045
dc.identifier.epage118
dc.identifier.scopus2-s2.0-84982108511
dc.identifier.spage103
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84982108511&doi=10.1016%2fj.asoc.2016.05.045&partnerID=40&md5=f76eb1499a01f385c4be4ef15bd86646
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22648
dc.identifier.volume47
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Soft Computing Journal
dc.titleA memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamformingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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