2020
DOI: 10.1109/jiot.2020.2983519
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Improving Performance of Distributed Collaborative Beamforming in Mobile Wireless Sensor Networks: A Multiobjective Optimization Method

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Cited by 29 publications
(12 citation statements)
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“…GA is a search algorithm based on the process of natural selection and natural genetics [21,33,34]. To name a few, many works have employed GA in beamforming applications such as in pattern synthesis [35], array thinning [36], beam and null steering [37], multiobjective optimization [38], and element failure correction ability [39].…”
Section: Genetic Algorithmmentioning
confidence: 99%
“…GA is a search algorithm based on the process of natural selection and natural genetics [21,33,34]. To name a few, many works have employed GA in beamforming applications such as in pattern synthesis [35], array thinning [36], beam and null steering [37], multiobjective optimization [38], and element failure correction ability [39].…”
Section: Genetic Algorithmmentioning
confidence: 99%
“…The scheme utilizes a hybrid meta-heuristic optimization algorithm: Particle Swarm Optimization and Gravitational Search Algorithm-Explore (PSOGSA-E) to optimize node transmit weights. In [18] , sidelobe minimization in CB through node position perturbation is proposed. The authors propose a multi-objective optimization approach aimed at concurrently optimizing peak sidelobe level, node transmit power and node motion energy.…”
Section: Introductionmentioning
confidence: 99%
“…Research towards peak sidelobe suppression in CB is active. A case in point is the work presented in [3,4] wherein a scheme towards sidelobe suppression through node location perturbation is presented. Although substantially good results are obtained, the proposed methods rely significantly on node mobility.…”
Section: Introductionmentioning
confidence: 99%